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Analyzed about 4 hours ago. based on code collected 1 day ago.
Nov 05, 2025 — Nov 05, 2026
Commit Message Contributor Files Modified Lines Added Lines Removed Code Location Date
Sections 1301,1302 updated
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1302. (THEORY and FEATURE) NeuronRainApps - Autonomous Driving - Obstacle Lattice from LIDAR Point Cloud Data - related to 1253,1294 and all sections on Async IO, Drone Electronic Voting Machines, GIS and Urban Sprawl Analytics, Merit of Large Scale Visuals, Drone Obstacle Avoidance, Computational Geometry, ADAS-Obstacle avoidance, Autonomous Vehicles, Unmanned Aerial Vehicle Navigation-Autonomous Drone Delivery-Passenger Drones, Wiring Problem
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------------------------------------------------------------------------------------------------------------------------------ 1302. (THEORY and FEATURE) NeuronRainApps - Autonomous Driving - Obstacle Lattice from LIDAR Point Cloud Data - related to 1253,1294 and all sections on Async IO, Drone Electronic Voting Machines, GIS and Urban Sprawl Analytics, Merit of Large Scale Visuals, Drone Obstacle Avoidance, Computational Geometry, ADAS-Obstacle avoidance, Autonomous Vehicles, Unmanned Aerial Vehicle Navigation-Autonomous Drone Delivery-Passenger Drones, Wiring Problem ------------------------------------------------------------------------------------------------------------------------------ 1302.1 This commit adds a NeuronRainApps LIDAR usecase for Autonomous Driving - python-src/NeuronRainApps/AutonomousDriving/AutonomousDriving.py - which reads a LIDAR LAS format file, detects Ground and Other Obstacles based on Return Number of LASER pulse and writes a Lattice obstacle matrix which could be read by latticewalks.cpp (NeuronRain Grafit - Section 1294) to compute Obstacle Free optimal lattice walks. 1302.2 Function read_lidar_stream() reads a LAS file simple.las (Laspy test dataset - https://github.com/laspy/laspy/blob/master/tests/data/simple.las) from LIDAR by laspy utility and finds points within a clearance query range parameter through KDTree representation of LAS point cloud. Rendering of simple.las Point Cloud through http://lidarview.com/ has been added to repository as PDF file simple.las.pdf showing number of returns and points per return number. 1302.3 It also differentiates points as Ground points and Obstacle points based on return number of the point and total number of returns of a LIDAR pulse reflected from surface. Mostly points of return number equal to number of returns are bottommost and reflected from ground while other points of less return number are obstacles (trees,buildings) which are iterated and populated in a lattice array after scaling (captured in testlogs/AutonomousDriving.log.5September2022). Lattice array requires Terabyte memoryview() allocation and has been commented.
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1301. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Polya Urn Urban Growth Model - Revised for N color segments - 2 September 2022 - related to 1174,1175,1179,1194,1211,1234,1290,1291,1292,1295,1296,1297 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from Urban sprawl demographics, electricity consumption, Poverty alleviation
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------------------------------------------------------------------------------------------------------------------------------- 1301. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Polya Urn Urban Growth Model - Revised for N color segments - 2 September 2022 - related to 1174,1175,1179,1194,1211,1234,1290,1291,1292,1295,1296,1297 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from Urban sprawl demographics, electricity consumption, Poverty alleviation ------------------------------------------------------------------------------------------------------------------------------- 1301.1 GISUrbanSprawlAnalytics.py Polya Urn Urban Growth Model function polya_urn_urban_growth_model(image,ncoloredsegments,segmentedgis,iterations=1000) has been changed to add image filename parameter and allow arbitrary number of colored contour segments. Contours segmented earlier from a GIS JPEG image are iterated and an n-colored dictionary of colors-to-segments is populated by looking up the color of contour centroid pixel in image ndarray construed as color of the contour segment. 1301.2 Contents of Polya Urn before and after growth (by choosing a random colored segment and replicating it) are printed. 1301.3 GHSL SMOD R2022A Degree of Urbanization JPEG image from https://ghsl.jrc.ec.europa.eu/ghs_smod2022.php for Chennai Metro Sprawl testlogs/RemoteSensingGIS/ChennaiMetropolitanArea_GHSL_R2022A_GHS_SMOD_DegreeOfUrbanisation.jpg which follows a 3-colored high level zoning (open space-water-vegetation, built space-residential, built space-non-residential) has been segmented for contours,watershed,canny edge detection and grown by Polya Urban Growth Model for 1000 iterations. Non-residential Built space includes both commercial and industrial areas. Following are affected image, DOT and log files: testlogs/ChennaiMetropolitanArea_GHSL_R2022A_GHS_SMOD_DegreeOfUrbanisation-cannyedgedetection.jpg testlogs/ChennaiMetropolitanArea_GHSL_R2022A_GHS_SMOD_DegreeOfUrbanisation-contourkmeansclustered.jpg testlogs/ChennaiMetropolitanArea_GHSL_R2022A_GHS_SMOD_DegreeOfUrbanisation-contourlabelled.jpg testlogs/ChennaiMetropolitanArea_GHSL_R2022A_GHS_SMOD_DegreeOfUrbanisation-contoursegmented.jpg testlogs/GISUrbanSprawlAnalytics.log.2September2022 testlogs/RemoteSensingGIS/ChennaiMetropolitanArea_GHSL_R2022A_GHS_SMOD_DegreeOfUrbanisation.jpg testlogs/RemoteSensingGIS/ChennaiMetropolitanArea_GHSL_R2022A_GHS_SMOD_DegreeOfUrbanisation_ImageNet_Keras_Theano_Segmentation_FaceGraph.dot testlogs/RemoteSensingGIS/ChennaiMetropolitanArea_GHSL_R2022A_GHS_SMOD_DegreeOfUrbanisation_segmented.jpg 1301.4 Differentiating Industrial and Commercial segments within Non-residential built space is non-trivial and necessary for 4-colored segmentation. While Polluting Manufacturing industries could be discerned by air quality index (PM2.5,PM10) from a datasource, for example, OpenWeatherMap Longitude-Latitude REST API - https://openweathermap.org/api/air-pollution, distinguishing non-polluting Manufacturing-IT-ITES from commercial activity is a problem unless they are of varying building heights or luminosity in NightLights data from VIIRS.
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Updated BestPractices.txt
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Sections 1253,1297 updated
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1297. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Facebook (Meta) High Resolution Settlement Layer (HRSL) - Population estimation from GDAL-Rasterio Georeferencing - 30 August 2022 - related to 1174,1175,1179,1194,1211,1290,1291,1292,1295,1296 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from Urban sprawl demographics, electricity consumption, Poverty alleviation
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------------------------------------------------------------------------------------------------------------------------------- 1297. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Facebook (Meta) High Resolution Settlement Layer (HRSL) - Population estimation from GDAL-Rasterio Georeferencing - 30 August 2022 - related to 1174,1175,1179,1194,1211,1290,1291,1292,1295,1296 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from Urban sprawl demographics, electricity consumption, Poverty alleviation ------------------------------------------------------------------------------------------------------------------------------- 1297.1 GISUrbanSprawlAnalytics.py has been updated to enhance urban_sprawl_from_segments() by adding a clause for looking up population value for a pixel in JPEG translation of a GeoTIFF raster by raster.xy() and compute population estimation based on it. 1297.2 Within translate_geotiff_to_jpeg(), gdal.Translate() has been changed to write JPEG file from GeoTIFF raster after scaling down X and Y sizes by function parameter scaledown. Scaling is necessary to reduce JPEG file sizes from actual raster dimensions. Scale parameters have been restored to [[0, 255, 0, 65535]] 1297.3 Function urban_sprawl_from_segments(image,segment,maximum_population_density=100000,sqkmtocontourarearatio=0,legend=None,sqkmareatopopulationratio=6.22,voronoi_delaunay=False,number_of_clusters=2,maxiterations=2,populationfromraster="None") has been updated to include an argument populationfromraster for name of raster TIF file. New if clause for reading a raster file has been introduced to find minimum enclosing circle of every contour in JPEG translation of TIF file and centroid of the enclosing circle. Pixel coodinates of contour centroids are lookeup in raster TIF of translated JPEG by xy() to find longitude-latitude after scaling up by factor scaleup to restore the coordinates to GeoTIFF dimensions as JPEG version is scaled down version of TIFF. 1297.4 Longitude-Latitude earlier is passed on to data_from_raster_georeferencing() to get population at the centroid coordinates within GeoTIFF raster. Scaling up and down earlier might cause some loss in pixel coordinate information because of integer division and actual longitude-latitude might vary and be inaccurate depending on scaling. Scaling has to be resorted to only when size of the raster and translated JPEG are huge and there are out of memory errors while loading. Setting scaleup and scaledown to 1 results in same size of JPEG as raster and doesn't distort the dimensions. 1297.5 Image file tokenization has been changed to remove hardcoded indices and replace them by last index of the tokens array in both ImageGraph_Keras_Theano.py and GISUrbanSprawlAnalytics.py. Array length check in contours kmeans clustering has been added. 1297.6 Following are logs,contour-segment-tessellation-edgedetection images and some example population estimates computed within testlogs/GISUrbanSprawlAnalytics.log.30August2022 for urban sprawl contour centroid long-lat (83.56,18.20) looking up Meta HRSL EPSG:4326 Raster FacebookMetaHRSL_IndiaPak_population_10_lon_80_general-v1.5.tif (not committed) bounded by longitudes 80-87 and latitudes 13-20 (unscaled GDAL translated JPEG testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_10_lon_80_general-v1.jpg is of size ~25 MB and slow to process which has been scaled to a factor of 50): testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_10_lon_80_general-v1.jpg testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_10_lon_80_general-v1.jpg.aux.xml testlogs/FacebookMetaHRSL_IndiaPak_population_10_lon_80_general-v1-cannyedgedetection.jpg testlogs/FacebookMetaHRSL_IndiaPak_population_10_lon_80_general-v1-contourkmeansclustered.jpg testlogs/FacebookMetaHRSL_IndiaPak_population_10_lon_80_general-v1-contourlabelled.jpg testlogs/FacebookMetaHRSL_IndiaPak_population_10_lon_80_general-v1-contoursegmented.jpg testlogs/GISUrbanSprawlAnalytics.log.30August2022 testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_10_lon_80_general-v1_ImageNet_Keras_Theano_Segmentation_FaceGraph.dot testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_10_lon_80_general-v1_segmented.jpg ====================testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_10_lon_80_general-v1.5.tif=================== == Raster bounds: BoundingBox(left=79.99819444444445, bottom=12.255416666660466, right=86.38041666667178, top=20.006527777777777) Raster height: 27904 Raster width: 22976 Raster top left - spatial value: (79.99833333333333, 20.00638888888889) Raster bottom right - spatial value: (86.38055555556066, 12.255277777771578) Raster transform: | 0.00, 0.00, 80.00| | 0.00,-0.00, 20.01| | 0.00, 0.00, 1.00| Raster CRS: EPSG:4326 Longitude: 83.60944444444733 Latitude: 19.77027777777759 Population from raster for longitude-latitude: [array([14.301587])] urban_sprawl_from_segments(): raster values = [array([14.301587])] ('Convex Hull of Urban Area:', array([[[ 19, 259]],
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1296. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Facebook (Meta) High Resolution Settlement Layer (HRSL) and Global Human Settlement Layer (GHSL) GDAL-Rasterio Georeferencing - Gini Index of Population and Built-Surface - 28 August 2022 - related to 1174,1175,1179,1194,1211,1290,1291,1292,1295 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from electricity consumption, Poverty alleviation
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------------------------------------------------------------------------------------------------------------------------------ 1296. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Facebook (Meta) High Resolution Settlement Layer (HRSL) and Global Human Settlement Layer (GHSL) GDAL-Rasterio Georeferencing - Gini Index of Population and Built-Surface - 28 August 2022 - related to 1174,1175,1179,1194,1211,1290,1291,1292,1295 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from electricity consumption, Poverty alleviation ------------------------------------------------------------------------------------------------------------------------------- 1296.1 GISUrbanSprawlAnalytics.py has been updated to define two new functions (*) urbansprawl_gini_coefficient(urbansprawldata) and (*) urban_sprawl_from_raster(longx,latx,longy,laty,raster,dt) for computing Gini Coefficient measure of inequality in an urban sprawl statistical parameter and to wrap the longitude-latitude urban sprawl bounding box computation of data_from_raster_georeferencing() for a raster respectively. 1296.2 urban_sprawl_from_raster() is computed on following 2 datasets of GHSL R2022A Built-Surface and Facebook Meta HRSL Indian subcontinent Population for Chennai Metropolitan Area Longitude-Latitude (80.2707,13.0827) Bounding Box - (80.1,13.0) to (80.3,13.2): urban_sprawl_from_raster(80.1,13.0,80.3,13.2,"testlogs/RemoteSensingGIS/GHS_BUILT_S_P2030LIN_GLOBE_R2022A_54009_100_V1_0_R8_C26.tif",dt="BUILT-S") urban_sprawl_from_raster(80.1,13.0,80.3,13.2,"testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_10_lon_80_general-v1.5.tif",dt="Population") 1296.3 Following excerpts of Gini coefficient computations from python-src/image_pattern_mining/ImageNet/testlogs/GISUrbanSprawlAnalytics.log.28August2022 for earlier Built-Surface and Population datasets ignore NaN values by math.isnan() filtering. Gini coefficient is defined as ratio of area bounded by hypotenuse and Lorenz curve to total area of right-angle triangle and ranges from 0 (complete equality) to 1 (complete inequality): Built-Surface - urbansprawl_gini_coefficient(): Gini Index of the dataset = 0.48700616284225207 (48.7%) Population - urbansprawl_gini_coefficient(): Gini Index of the dataset = 0.28008540547871624 (28.0%) ignoring NaNs. 1296.4 Disparity of 48.7% and 28% between Built-Surface (less equality) and Population (more equality) implies construction activity is not commensurate and hasn't caught up with burgeoning population.
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Updated Requirements.txt
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1295. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Facebook (Meta) High Resolution Settlement Layer (HRSL) and Global Human Settlement Layer (GHSL) GDAL-Rasterio Georeferencing - Sampling and Mollweide-EPSG transforms reprojections - 25 August 2022 - related to 1174,1175,1179, 1194,1211,1290,1291,1292 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GP W4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, R Rick er Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Compu tational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from electricity consumption
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------------------------------------------------------------------------------------------------------------------------------ 1295. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Facebook (Meta) High Resolution Settlement Layer (HRSL) and Global Human Settlement Layer (GHSL) GDAL-Rasterio Georeferencing - Sampling and Mollweide-EPSG transforms reprojections - 25 August 2022 - related to 1174,1175,1179,1194,1211,1290,1291,1292 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from electricity consumption ------------------------------------------------------------------------------------------------------------------------------ 1295.1 GISUrbanSprawlAnalytics.py now imports fiona transforms and rasterio sample modules. Function data_from_raster_georeferencing(geotifffile,shapes=False,longitude=None,latitude=None,bandnum=1,datatype="Population",windowslice=100,sample=False) has been updated to add sample as a parameter. 1295.2 New if-else clause has been introduced which checks the CRS of the raster and if crs is (*) EPSG:4326 and if sample is enabled raster is sampled by sample_gen for data at a longitude-latitude coordinate and if sample is disabled usual window read scan of whole raster is performed to search for data at longitude-latitude (*) other than EPSG:4326 which could most likely be mollweide, EPSG:4326 source longitude-latitude reference system is transformed to destination mollweide reference system ESRI:54009 by reprojection and data is sampled by sample_gen at transformed coordinates. 1295.3 data_from_raster_georeferencing() is invoked on two datasets : (*) HRSL Indian subcontinent population estimate GeoTIFF - testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_10_lon_80_general-v1.5.tif by iterating over a rectangle of longitude-latitude bounding box ensconcing Chennai Metropolitan Area - bounded by topleft-(80.2,13.0) and bottomright-(80.3,13.2) to print population data sampled in rectangle - iteration uses arange() as Python range() is limited to integer values (*) Newly released GHSL R2022A Built Surface dataset testlogs/RemoteSensingGIS/GHS_BUILT_S_P2030LIN_GLOBE_R2022A_54009_100_V1_0_R8_C26.tif for sampling built surface data at longitude=80.2707,latitude=13.0827 of Chennai Metropolitan Agglomeration. 1295.4 Following are logs quoted from testlogs/GISUrbanSprawlAnalytics.log.25August2022 for HRSL and GHSL GeoTIFFs: ... ==================testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_10_lon_80_general-v1.5.tif===================== Raster bounds: BoundingBox(left=79.99819444444445, bottom=12.255416666660466, right=86.38041666667178, top=20.006527777777777) Raster height: 27904 Raster width: 22976 Raster top left - spatial value: (79.99833333333333, 20.00638888888889) Raster bottom right - spatial value: (86.38055555556066, 12.255277777771578) Raster transform: | 0.00, 0.00, 80.00| | 0.00,-0.00, 20.01| | 0.00, 0.00, 1.00| Raster CRS: EPSG:4326 Longitude: 80.29000000000005 Latitude: 13.119999999999997 Population from raster for longitude-latitude: [array([48.887234])] ====================testlogs/RemoteSensingGIS/GHS_BUILT_S_P2030LIN_GLOBE_R2022A_54009_100_V1_0_R8_C26.tif===================== Raster bounds: BoundingBox(left=7059000.0, bottom=1000000.0, right=7959000.0, top=2000000.0) Raster height: 10000 Raster width: 9000 Raster top left - spatial value: (7059050.0, 1999950.0) Raster bottom right - spatial value: (7959050.0, 999950.0) Raster transform: | 100.00, 0.00, 7059000.00| | 0.00,-100.00, 2000000.00| | 0.00, 0.00, 1.00| Raster CRS: ESRI:54009 Longitude: 80.2707 Latitude: 13.0827 Source CRS: EPSG:4326 Destination CRS: +proj=moll +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs=True Transformed Coordinates: [[7915402.10721301, 1612214.7819872624]] BUILT from raster: [array([3699], dtype=uint16)]
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Section 1292 updated
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+------------------------------------------------------------------------------------------------------------------------------ +1292. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Facebook (Meta) High Resolution Settlement Layer (HRSL) and LandSat 9 OLI2-TIRS2 GDAL-Rasterio Georeferencing - Window read - 22 August 2022 - related to 1174,1175,1179,1194,1211,1290,1291 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from electricity consumption +------------------------------------------------------------------------------------------------------------------------------
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------------------------------------------------------------------------------------------------------------------------------ 1292. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Facebook (Meta) High Resolution Settlement Layer (HRSL) and LandSat 9 OLI2-TIRS2 GDAL-Rasterio Georeferencing - Window read - 22 August 2022 - related to 1174,1175,1179,1194,1211,1290,1291 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from electricity consumption ------------------------------------------------------------------------------------------------------------------------------ 1292.1 GISUrbanSprawlAnalytics.py has been updated to rename and add parameters to function population_estimate_from_raster_georeferencing(geotifffile,longitude=None,latitude=None) as generic data_from_raster_georeferencing(geotifffile,shapes=False,longitude=None,latitude=None,bandnum=1,datatype="Population",windowslice=100) to facilitate window read of huge GeoTIFF raster files which cause VSIMalloc3() allocation memory errors within gdalnodatamaskband.cpp of GDAL C++ (known issue). Tuning GDAL_CACHEMAX does not solve the exception. 1292.2 Boolean flag shapes optionally enables or disables masking of the raster to remove unnecessary pixels and computation of polygon shapes from raster. Parameter bandnum determines band number of the raster and windowslice configures the number of horizontal and vertical slices to tile huge raster files into smaller rectangular windows. Type of data analyzed is denoted by "datatype" argument (set to "Population" as default, other value being "Radiance"). 1292.3 Rectangular window chunks are read from raster in 2 for loops by raster.read() and longitude-latitude value is searched in each window by index() which throws an exception if search fails. Exception is ignored and exhaustive search resumes(a Duff's device) scanning the whole raster till match is found. 1292.4 Function translate_geotiff_to_jpeg(geotifffile,display=True,topx=0,topy=0,bottomx=1000,bottomy=1000,bandnum=1) has been updated to replace hardcoded band number. GDAL translate scaleParams are sourced from GetStatistics() as gdal.Translate("testlogs/RemoteSensingGIS/"+jpegfilename[2] + ".jpg",geotifffile,format='JPEG', width=1024, height=0, scaleParams=[[statistics[0],statistics[1]]], outputType = gdal.GDT_UInt16) 1292.5 3 GeoTIFF files (*) HRSL - testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_10_lon_80_general-v1.5.tif (*) LandSat 9 OLI2-TIRS2 Chennai Metropolitan Area Downwell Radiance - testlogs/RemoteSensingGIS/LC08_L2SP_142051_20220729_20220806_02_T1_ST_DRAD.TIF (*) LandSat 9 OLI2-TIRS2 Chennai Metropolitan Area - Band 7 - testlogs/RemoteSensingGIS/LC08_L2SP_142051_20220729_20220806_02_T1_SR_B7.TIF have been translated to JPEG and Georeferenced as below: translate_geotiff_to_jpeg("testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_10_lon_80_general-v1.5.tif") data_from_raster_georeferencing("testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_10_lon_80_general-v1.5.tif",80.2707,13.0827) translate_geotiff_to_jpeg("testlogs/RemoteSensingGIS/LC08_L2SP_142051_20220729_20220806_02_T1_ST_DRAD.TIF") data_from_raster_georeferencing("testlogs/RemoteSensingGIS/LC08_L2SP_142051_20220729_20220806_02_T1_ST_DRAD.TIF",bandnum=1,datatype="Radiance") translate_geotiff_to_jpeg("testlogs/RemoteSensingGIS/LC08_L2SP_142051_20220729_20220806_02_T1_SR_B7.TIF") data_from_raster_georeferencing("testlogs/RemoteSensingGIS/LC08_L2SP_142051_20220729_20220806_02_T1_SR_B7.TIF",bandnum=1,datatype="Radiance") 1292.6 LandSat 9 imagery by OLI2-TIRS2 instruments acquired on 29 July 2022 are sourced from USGS EarthExplorer - https://landsatlook.usgs.gov/gen-browse?size=rrb&type=refl&product_id=LC08_L1TP_142051_20220729_20220806_02_T1
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Updated Requirements.txt
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1291. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Facebook (Meta) High Resolution Settlement Layer (HRSL) Population Estimation from GDAL-Rasterio Georeferencing - 19 August 2022 - related to 1174,1175,1179,1194,1211,1290 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from electricity consumption
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------------------------------------------------------------------------------------------------------------------------------- 1291. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Facebook (Meta) High Resolution Settlement Layer (HRSL) Population Estimation from GDAL-Rasterio Georeferencing - 19 August 2022 - related to 1174,1175,1179,1194,1211,1290 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from electricity consumption ------------------------------------------------------------------------------------------------------------------------------- 1291.1 New function population_estimate_from_raster_georeferencing(geotifffile,longitude=None,latitude=None) has been implemented in GISUrbanSprawlAnalytics.py which imports Rasterio python bindings for GDAL Georeferencing and computes following for a GeoTIFF (invoked for HRSL raster testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_30_lon_80_general-v1.5.tif): 1291.1.1 Coordinate Polygons in the Raster by Rasterio.shapes and Warp() longitude-latitude EPSG:4326 transformations for the polygons which could be the buildings and land usage patterns in zone segment classifications - Residential,Commercial,Industries and Greenery - could be useful for computing land usage percentages. 1291.1.2 Population value for a longitude-latitude from HRSL Raster GeoTIFF Georeferencing (pixels to Coordinate reference frame and viceversa) by index() function which maps to a (row,column) pixel on band 1 of the raster data. 1291.2 More statistics have been computed within translate_geotiff_to_jpeg(geotifffile,display=True,topx=0,topy=0,bottomx=1000,bottomy=1000) 1291.3 Smallest size GeoTIFF file has been chosen which contains NaNs as Larger TIFFs are memory intensive and cause segfault.Following are lo gs,tif and xml files generated: testlogs/GISUrbanSprawlAnalytics.log.19August2022 testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_30_lon_80_general-v1.5.tif testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_30_lon_80_general-v1.5.tif.aux.xml testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_30_lon_80_general-v1.jpg.aux.xml
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Updated Requirements.txt
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------------------------------------------------------------------------------------------------------------------------------ 1290. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Facebook (Meta) High Resolution Settlement Layer (HRSL) Population Density Maps - GDAL GeoTIFF to JPEG format Translation - 18 August 2022 - related to 1174,1175,1179,1194,1211 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from electricity consumption ------------------------------------------------------------------------------------------------------------------------------
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------------------------------------------------------------------------------------------------------------------------------ 1290. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Facebook (Meta) High Resolution Settlement Layer (HRSL) Population Density Maps - GDAL GeoTIFF to JPEG format Translation - 18 August 2022 - related to 1174,1175,1179,1194,1211 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from electricity consumption ------------------------------------------------------------------------------------------------------------------------------ 1290.1 GDAL integration which was abandoned due to series of installation issues has been fulfilled after resolving compilation errors based on documentation from https://mothergeo-py.readthedocs.io/en/latest/development/how-to/gdal-ubuntu-pkg.html - libgdal-dev has been compiled after setting env: export CPLUS_INCLUDE_PATH=/usr/include/gdal export C_INCLUDE_PATH=/usr/include/gdal pip3.7 install GDAL==2.4.2 1290.2 GDAL C++-Python bindings are imported from osgeo and new function translate_geotiff_to_jpeg(geotifffile,display=True,topx=0,topy=0,bottomx=1000,bottomy=1000) has been defined which loads a GeoTIFF file by GDAL Open() and translates it to jpeg format by GDAL Translate() 1290.3 Function earlier optionally displays the GeoTIFF file in a bounding box (topx,topy,bottomx,bottomy) and its statistics by Raster X-Y sizes, Bands and GetStatistics() if display=True. 1290.4 World's most accurate population GIS - Facebook - Data For Good at Meta - HRSL 2021 - "HDX - Pakistan & India: High Resolution Population Density Maps - General" from https://data.humdata.org/dataset/2c8cfe67-448f-4a27-94b3-aaa944598cba/resource/e3dbe673-4117-4243-93a8-3366c6e6396b/download/population_ind_pak_general.zip have been downloaded in GeoTIFF format (523.1 MB) and translated to JPEG format by translate_geotiff_to_jpeg(). Display of this high resolution dataset in matplotlib requires 6.92 GB of array memory size per GeoTIFF and might fail in normal desktop configurations and bounding boxes for smaller sizes may not display. This GeoTIFF data set is single band and only grayscale JPEG rendering is possible. 1290.5 Following are the logs and JPG files categorized by grid of longitudes and latitudes translated from HRSL GeoTIFFs: testlogs/GISUrbanSprawlAnalytics.log.18August2022 testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_0_lon_70_general-v1.jpg testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_0_lon_80_general-v1.jpg testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_0_lon_90_general-v1.jpg testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_10_lon_70_general-v1.jpg testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_10_lon_80_general-v1.jpg testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_10_lon_90_general-v1.jpg testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_20_lon_60_general-v1.jpg testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_20_lon_70_general-v1.jpg testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_20_lon_80_general-v1.jpg testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_20_lon_90_general-v1.jpg testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_30_lon_60_general-v1.jpg testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_30_lon_70_general-v1.jpg testlogs/RemoteSensingGIS/FacebookMetaHRSL_IndiaPak_population_30_lon_80_general-v1.jpg
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Section 1289 updated
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1289. (THEORY and FEATURE) Astronomy and Cosmology Datasets Analytics - N-Body gravitational accelerations computed for USGS (1900-2012) 8+ magnitude Earthquakes and NOAA HURDAT2 (1851-2012) North Atlantic Hurricanes Datasets - 16 August 2022 - related to 17,137,148,445,731,1206,1236,1255,1266,1267,1268,1270,1271,1272,1273 and all sections on Astronomical Dataset Mining, Archaeoastronomical Dating, Sequence Mining, String analytics, Sequence Alignment, Visual Planetarium Search, Archaeology, Climate and Extreme Weather Event Analytics, Computational Astrophysics, N-Body problem, Merit of Large Scale Visuals, Astronomical algorithms, Histogram analytics
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------------------------------------------------------------------------------------------------------------------------------ 1289. (THEORY and FEATURE) Astronomy and Cosmology Datasets Analytics - N-Body gravitational accelerations computed for USGS (1900-2012) 8+ magnitude Earthquakes and NOAA HURDAT2 (1851-2012) North Atlantic Hurricanes Datasets - 16 August 2022 - related to 17,137,148,445,731,1206,1236,1255,1266,1267,1268,1270,1271,1272,1273 and all sections on Astronomical Dataset Mining, Archaeoastronomical Dating, Sequence Mining, String analytics, Sequence Alignment, Visual Planetarium Search, Archaeology, Climate and Extreme Weather Event Analytics, Computational Astrophysics, N-Body problem, Merit of Large Scale Visuals, Astronomical algorithms, Histogram analytics ------------------------------------------------------------------------------------------------------------------------------ 1289.1 EphemerisSearch.py has been updated to change and add new parameters to function extreme_weather_events_n_body_analytics(self,datesofEWEs=None,loc="@earth-moon",angularsep=False,maxiterations=100) for restricting the number of dates of extreme weather events (EWE) iterated, choice of angular separation computation by boolean flag angularsep and 3 types of values for datesofEWEs ("Earthquakes","Hurricanes",User supplied dates of EWEs) 1289.2 For datesofEWEs="Earthquakes", datetimes are sourced from USGS 8.0+ earthquakes dataset (1900-2012) which has already been ETLed as textfile in python-src/earthquakesFrom1900with8plusmag.pygen.txt. For datesofEWEs="Hurricanes", datetimes are sourced from NOAA Hurricane Dataset HURDAT2 already ETLed as textfile in python-src/hurdat2_1851_2012-jun2013.pygen.txt. All datetimes are cast to UTC format compatible with JPL Horizons Ephemeris Service. For dateofEWEs supplied by user as array, computations are as usual with no change. 1289.3 N-Body Solar system gravitational accelerations are computed at Earth-Moon barycenter for USGS Earthquake datasets and with respect to respective longitude-latitude(s) of formation and zero elevation(sea level) for NOAA HURDAT2 Hurricane datasets (restricted to 100 hurricanes as the dataset is huge containing 50000 hurricanes spanning 161 years). This complements String Encoding and GSP Sequence Mining of USGS and NOAA HURDAT2 datasets in python-src/autogen_classifier_dataset/ and documented at asfer-docs/NeuronRain_Research_Celestial_Pattern_Mining.txt 1289.4 Computations of N-Body gravitational accelerations for USGS(Earthquake) and NOAA(Hurricane) datasets have been captured separately in following files: python-src/testlogs/EphemerisSearch.log.Earthquakes.16August2022 python-src/testlogs/EphemerisSearch.log.Hurricanes.16August2022 1289.5 Following are excerpts of computations respectively for Sendai Earthquake on 11 March 2011 (9.0) and first recorded Hurricane (25 June 1851): --------------------------------------------------------------------------------- extreme_weather_events_n_body_analytics(): gravity of Sun at @earth-moon on (2011, 3, 11, 5, 46, 0) : [-3.60092543e-06 -9.97593306e-06 7.52550118e-08] extreme_weather_events_n_body_analytics(): gravity of Moon at @earth-moon on (2011, 3, 11, 5, 46, 0) : [ 5.93284102e-03 -1.07378201e-03 -6.98565223e-07] extreme_weather_events_n_body_analytics(): gravity of Mars at @earth-moon on (2011, 3, 11, 5, 46, 0) : [-2.86408002e-03 1.21006257e-03 9.57056611e-05] extreme_weather_events_n_body_analytics(): gravity of Mercury at @earth-moon on (2011, 3, 11, 5, 46, 0) : [-0.04248091 -0.04133797 0.00052057] extreme_weather_events_n_body_analytics(): gravity of Jupiter at @earth-moon on (2011, 3, 11, 5, 46, 0) : [-2.35324463e-04 -5.80881981e-05 5.50732456e-06] extreme_weather_events_n_body_analytics(): gravity of Venus at @earth-moon on (2011, 3, 11, 5, 46, 0) : [ 3.89399938e-03 1.05719009e-02 -7.99002829e-05] extreme_weather_events_n_body_analytics(): gravity of Saturn at @earth-moon on (2011, 3, 11, 5, 46, 0) : [ 6.26683011e-05 1.43778659e-05 -2.74496998e-06] extreme_weather_events_n_body_analytics(): gravity of Uranus at @earth-moon on (2011, 3, 11, 5, 46, 0) : [-1.47148302e-05 -7.17547152e-08 1.90377268e-07] extreme_weather_events_n_body_analytics(): gravity of Neptune at @earth-moon on (2011, 3, 11, 5, 46, 0) : [-5.61349018e-06 3.45933185e-06 5.81269242e-08] extreme_weather_events_n_body_analytics(): gravity of Pluto at @earth-moon on (2011, 3, 11, 5, 46, 0) : [-5.46414412e-07 5.75922735e-06 -4.58472382e-07] --------------------------------------------------------------------------------- extreme_weather_events_n_body_analytics(): gravity of Sun at {'lon': 28.0, 'lat': 94.8, 'elevation': 0.0} on (1851, 6, 25, 0, 0, 0) : [ 9.38057871e-06 5.60142035e-06 -4.73504468e-07] extreme_weather_events_n_body_analytics(): gravity of Moon at {'lon': 28.0, 'lat': 94.8, 'elevation': 0.0} on (1851, 6, 25, 0, 0, 0) : [-5.00327362e-04 5.73733377e-03 3.17136748e-06] extreme_weather_events_n_body_analytics(): gravity of Mars at {'lon': 28.0, 'lat': 94.8, 'elevation': 0.0} on (1851, 6, 25, 0, 0, 0) : [-2.89680838e-03 -7.70315656e-04 5.57099428e-05] extreme_weather_events_n_body_analytics(): gravity of Mercury at {'lon': 28.0, 'lat': 94.8, 'elevation': 0.0} on (1851, 6, 25, 0, 0, 0) : [-0.04498039 0.00424941 0.00449187] extreme_weather_events_n_body_analytics(): gravity of Jupiter at {'lon': 28.0, 'lat': 94.8, 'elevation': 0.0} on (1851, 6, 25, 0, 0, 0) : [ 1.79703311e-04 8.78879940e-05 -4.38943473e-06] extreme_weather_events_n_body_analytics(): gravity of Venus at {'lon': 28.0, 'lat': 94.8, 'elevation': 0.0} on (1851, 6, 25, 0, 0, 0) : [-0.00965776 -0.0058695 0.0004808 ] extreme_weather_events_n_body_analytics(): gravity of Saturn at {'lon': 28.0, 'lat': 94.8, 'elevation': 0.0} on (1851, 6, 25, 0, 0, 0) : [-6.04252762e-05 -3.29543490e-05 2.97786557e-06] extreme_weather_events_n_body_analytics(): gravity of Uranus at {'lon': 28.0, 'lat': 94.8, 'elevation': 0.0} on (1851, 6, 25, 0, 0, 0) : [-1.26534427e-05 -8.24679688e-06 1.33372066e-07] extreme_weather_events_n_body_analytics(): gravity of Neptune at {'lon': 28.0, 'lat': 94.8, 'elevation': 0.0} on (1851, 6, 25, 0, 0, 0) : [-6.19751174e-06 2.31563273e-06 9.50902396e-08] extreme_weather_events_n_body_analytics(): gravity of Pluto at {'lon': 28.0, 'lat': 94.8, 'elevation': 0.0} on (1851, 6, 25, 0, 0, 0) : [-2.02686099e-06 -1.24640855e-06 7.19837809e-07] 1289.6 It can be observed that the gravitational acceleration vector components of solar system bodies decrease in value exponentially proportionate to their distance from earth except Mercury anomaly of which is unusually high.
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+------------------------------------------------------------------------------------------------------------------------------- +1288. (THEORY and FEATURE) Merit of Large Scale Visuals - Archaeology - Rebus Decipherments of Indus Pashupathi Seals and mining frequent subgraphs from predictions - 10 August 2022 - related to 1203,1232,1233,1236,1237,1240,1287 and all sections on Merit of Large Scale Visuals, Physique Recognition From Quadrilateral mesh isomorphism,Topological Face and Handwriting recognition, Archaeology-Topological Script Recognition, Product Homotopy, Homeomorphism and Topological Deformations, People Analytics, Computational Geometry, Graph Edit Distance and Earth mover distance, Graph Isomorphism (Exact, Approximate and Subgraph),Graph matching,Graph similarity,Image Search,Quadrilateral Mesh Deformation and Isomorphism,Maximum Common Subgraph Problem and Approximate Topological Matching +--------------------------------------------------------------------------------------------------------------------------------
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------------------------------------------------------------------------------------------------------------------------------- 1288. (THEORY and FEATURE) Merit of Large Scale Visuals - Archaeology - Rebus Decipherments of Indus Pashupathi Seals and mining frequent subgraphs from predictions - 10 August 2022 - related to 1203,1232,1233,1236,1237,1240,1288 and all sections on Merit of Large Scale Visuals, Physique Recognition From Quadrilateral mesh isomorphism,Topological Face and Handwriting recognition, Archaeology-Topological Script Recognition, Product Homotopy, Homeomorphism and Topological Deformations, People Analytics, Computational Geometry, Graph Edit Distance and Earth mover distance, Graph Isomorphism (Exact, Approximate and Subgraph),Graph matching,Graph similarity,Image Search,Quadrilateral Mesh Deformation and Isomorphism,Maximum Common Subgraph Problem and Approximate Topological Matching -------------------------------------------------------------------------------------------------------------------------------- 1288.1 In continuation of ResNet50 analysis of Indus-Sarasvati Pashupathi seal in 1287,this update computes the frequent subgraphs in textgraphs of decode_predictions() for 2 very similar Pashupathi seals (seals 80 and 81 of Plate 4 in Appendix 3 of concordance table) python-src/image_pattern_mining/ImageNet/testlogs/yogic-seal_0.jpg and python-src/image_pattern_mining/ImageNet/testlogs/yogic-seal_1.jpg having similar figurines but different script motifs except symbols 59 and 342 of Concordance table which are common to both the seals. By rebus principle, common motifs in two artefacts should result in common predictions corresponding to the common motifs. 1288.2 Following are the frequent subgraphs mined from predictions textgraphs of the two Pashupathi seals which contain prominent vertices for brass,buckles,pickelhaube,stupa(a religious structure),shield,helmet etc.,: Mined Frequent Subgraph Edges: {('buckles', 'buckle'), ('base', 'support'), ('cup-shaped', 'brass'), ('means', 'brass'), ('spiked', 'pickelhaube'), ('pickelhaube', 'worn'), ('buckle', 'buckle'), ('covering', 'shield'), ('pickelhaube', 'German'), ('Buddhists', 'stupa'), ('shield', 'covering'), ('worn', 'pickelhaube'), ('brass', 'variable'), ('brass', 'means'), ('stupa', 'dome-shaped'), ('support', 'base'), ('stupa', 'shrine'), ('brass', 'instrument'), ('blown', 'brass'), ('stupa', 'erected'), ('pickelhaube', 'soldiers'), ('wind', 'brass'), ('stupa', 'Buddhists'), ('shrine', 'stupa'), ('erected', 'stupa'), ('fasten', 'buckle'), ('soldiers', 'pickelhaube'), ('brass', 'wind'), ('brass', 'usually'), ('brass', 'blown'), ('consists', 'brass'), ('length', 'brass'), ('dome-shaped', 'stupa'), ('mouthpiece', 'brass'), ('instrument', 'brass'), ('brass', 'cup-shaped'), ('foundation', 'base'), ('pickelhaube', 'spiked'), ('brass', 'brass'), ('brass', 'mouthpiece'), ('tube', 'brass'), ('brass', 'funnel-shaped'), ('structure', 'shield'), ('brass', 'length'), ('pickelhaube', 'helmet'), ('buckle', 'fasten'), ('protective', 'shield'), ('shield', 'protective'), ('funnel-shaped', 'brass'), ('variable', 'brass'), ('base', 'foundation'), ('usually', 'brass'), ('shield', 'structure'), ('helmet', 'pickelhaube'), ('buckle', 'buckles'), ('brass', 'tube'), ('brass', 'consists'), ('German', 'pickelhaube')} 1288.3 Insight in earlier common subgraph of the two seals is it has in it buried the meaning of symbols 59 and 342.
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Updated NeuronRainUsecases.txt
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+------------------------------------------------------------------------------------------------------------------------------ +1287. (THEORY and FEATURE) Merit of Large Scale Visuals - Archaeology - Rebus Decipherment of Indus Pashupathi Seal - 9 August 2022 - related to 1203,1232,1233,1236,1237,1240 and all sections on Merit of Large Scale Visuals, Physique Recognition From Quadrilateral mesh isomorphism,Topological Face and Handwriting recognition, Archaeology-Topological Script Recognition, Product Homotopy, Homeomorphism and Topological Deformations, People Analytics, Computational Geometry, Graph Edit Distance and Earth mover distance, Graph Isomorphism (Exact, Approximate and Subgraph),Graph matching,Graph similarity,Image Search,Quadrilateral Mesh Deformation and Isomorphism,Maximum Common Subgraph Problem and Approximate Topological Matching +--------------------------------------------------------------------------------------------------------------------------------
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