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Analyzed about 8 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
Updated Requirements.txt
Krishna iResearch - NeuronRain - K.Srinivasan
More... almost 5 years ago
Section 629 updated - RZFSAT
Krishna iResearch - NeuronRain - K.Srinivasan
More... almost 5 years ago
+--------------------------------------------------------------------------------------------------------------------------------------------- +1173. (THEORY and FEATURE) Large Scale Visuals Streaming NoSQL Datastore (MongoDB-GridFS) support in Streaming_AbstractGenerator Facade - related to 1 and all sections on BigData NoSQL backend, Streaming analytics, Merit of Large Scale Visuals, Compressed Sensing, GIS Urban Planning Analytics, GIS Weather Analytics, Drone Imagery Analytics, Online shopping delivery, Obstacle avoidance - 15 November 2021 +---------------------------------------------------------------------------------------------------------------------------------------------
Krishna iResearch - NeuronRain - K.Srinivasan
More... almost 5 years ago
--------------------------------------------------------------------------------------------------------------------------------------------- 1173. (THEORY and FEATURE) Large Scale Visuals Streaming NoSQL Datastore (MongoDB-GridFS) support in Streaming_AbstractGenerator Facade - related to 1 and all sections on BigData NoSQL backend, Streaming analytics, Merit of Large Scale Visuals, Compressed Sensing, GIS Urban Planning Analytics, GIS Weather Analytics, Drone Imagery Analytics, Online shopping delivery, Obstacle avoidance - 15 November 2021 --------------------------------------------------------------------------------------------------------------------------------------------- 1173.1 Streaming_AbstractGenerator.py facade design pattern has been updated with a clause for GIS and Visuals streaming on MongoDB-GridFS backend which facilitates storage of large visuals (> 16 MB) 1173.2 Within Streaming_AbstractGenerator.py, MongoDB GIS and Large Scale Visuals database "neuronraingis" has been created with a bucket id "bucket1" which stores various weather GIS visuals uploaded manually by Robomongo3T-Studio3T GUIs (could be automated through a standalone python client code). StreamingAbsGen() object is instantiated within GISWeatherAnalytics.py by passing the datasource (GISAndVisualStreaming), datastorage (MongoDB) and MongoDB GridFS Bucket (bucket1) as arguments. The __init__() and __iter__() methods of StreamingAbsGen class have been changed for member variables and iterator yield by enumerating items in GridFS bucket. 1173.3 Choice of an optimal datastore for streaming image files has been contentious and use of databases for storing large images has been discouraged. For example, Netflix OTT platform implements a proprietary datastore NMDB which ingests huge movie files and keeps track of customer billing optimized for realtime response. NoSQL choices are of widerange from ElasticSearch to MongoDB-Kafka-Kinesis-Cassandra to Hadoop-Sparrk Parquets. Many video streaming platforms are hosted on proprietary AWS Kinesis and S3. 1173.4 NeuronRain analytics which transcends the whole gamut of software stack from application layer machine learning---middleware---OS kernel cloud RPC system calls-drivers-scheduler requires a robust performant opensource backend which serves large files especially images and videos in realtime with low latency. MongoDB's GridFS which supports huge files is an opensource alternative. By this commit, NeuronRain-MongoDB-GridFS could be termed an enterprise-grade streaming and OTT platform for deploying and streaming large visuals including movies. 1173.5 Downloaded image files from MongoDB are read from GridOut object and cast to bytes by io.BytesIO(). PILlow Image.open() is invoked to read the image bytestream which is then written to a temporary file name testlogs/GISAndVisualStreamNoSQLStore.jpg for use by weather analytics. Previous pattern of upload and iterator download of large scale visuals through Streaming_AbstractGenerator might have to be replicated throughout NeuronRain image analytics - topological face recognition and handwriting recognition, GIS satellite imagery for remote sensing, urban sprawls and weather. 1173.6 Compressed Sensing sketch for compressing-decompressing large image files has been minimally implemented in NeuronRain. For minimizing storage, GIS and large visuals might have to be compressed and stored. 1173.7 Drone imagery analytics for Online Shopping Delivery and Obstacle avoidance is another arena which requires realtime analytics of streaming images transmitted by drone. Images from on-flight drone have to be segmented live to find natural,weather and man-made obstacles and accordingly alter drone trajectory. 1173.8 MongoDB-GridFS BigData backend earlier could well be suitable for any other bigdata not just limited to visuals - Sifting large volumes of emails and documentation requires storage which is non-proprietary. There are opensource web email and repository linked web hosting providers like www.protonmail.com and readthedocs.org which could be feeds to MongoDB-GridFS backend. 1173.9 Following are the image and logs files created for this code change: image_pattern_mining/ImageNet/testlogs/GISAndVisualStreamNoSQLStore-contourlabelled.jpg image_pattern_mining/ImageNet/testlogs/GISAndVisualStreamNoSQLStore-inverted.jpg image_pattern_mining/ImageNet/testlogs/GISAndVisualStreamNoSQLStore.jpg image_pattern_mining/ImageNet/testlogs/GISWeatherAnalytics.log.15November2021 image_pattern_mining/ImageNet/testlogs/RemoteSensingGIS/GISAndVisualStreamNoSQLStore_FaceGraph.jpg image_pattern_mining/ImageNet/testlogs/RemoteSensingGIS/GISAndVisualStreamNoSQLStore_ImageNet_Keras_Theano_Segmentation_FaceGraph.dot image_pattern_mining/ImageNet/testlogs/RemoteSensingGIS/GISAndVisualStreamNoSQLStore_contour_segmented.jpg image_pattern_mining/ImageNet/testlogs/RemoteSensingGIS/GISAndVisualStreamNoSQLStore_convex_hull_segmented.jpg image_pattern_mining/ImageNet/testlogs/RemoteSensingGIS/GISAndVisualStreamNoSQLStore_segmented.jpg
Srinivasan Kannan
More... almost 5 years ago
Section 629 updated - RZFSAT
Krishna iResearch - NeuronRain - K.Srinivasan
More... almost 5 years ago
Section 1171 updated
Krishna iResearch - NeuronRain - K.Srinivasan
More... almost 5 years ago
Section 1171 updated
Krishna iResearch - NeuronRain - K.Srinivasan
More... almost 5 years ago
Section 1171 updated
Krishna iResearch - NeuronRain - K.Srinivasan
More... almost 5 years ago
--------------------------------------------------------------------------------------------------------------------------------------------- +1172. (THEORY and FEATURE) GIS Weather Analytics and Image grayscale inversion - implementation - related to 445,446,463,1165,1171 and all sections on GIS and Urban Sprawl Analytics, Mining patterns from Astronomical Datasets for correlating celestial-terrestrial events, Machine Learning solutions to N-Body problem, Merit of Large Scale Visuals, Space filling by Curves, Unequal Circle and Polynomial packing, Chaos and Mandelbrot sets, Computational Geometry, Drone Obstacle Avoidance, Autonomous Online Shopping Delivery - 11 November 2021 +---------------------------------------------------------------------------------------------------------------------------------------------
Krishna iResearch - NeuronRain - K.Srinivasan
More... almost 5 years ago
--------------------------------------------------------------------------------------------------------------------------------------------- 1172. (THEORY and FEATURE) GIS Weather Analytics and Image grayscale inversion - implementation - related to 445,446,463,1165,1171 and all sections on GIS and Urban Sprawl Analytics, Mining patterns from Astronomical Datasets for correlating celestial-terrestrial events, Machine Learning solutions to N-Body problem, Merit of Large Scale Visuals, Space filling by Curves, Unequal Circle and Polynomial packing, Chaos and Mandelbrot sets, Computational Geometry, Drone Obstacle Avoidance, Autonomous Online Shopping Delivery - 11 November 2021 --------------------------------------------------------------------------------------------------------------------------------------------- 1172.1 Weather Forecast is an indispensable element of Urban planning, Disaster Response, Agriculture Warning Systems among others. Finding weather obstacle segments - clouds and winds - from Weather GIS imagery is essential for safe navigation of autonomous aerial vehicles. Study of Orderly Disorder in Long term weather forecast gave birth to Deterministic Chaos theory (Lorenz attractors - https://en.wikipedia.org/wiki/Edward_Norton_Lorenz) 1172.2 Because of separate modelling involved in predicting weather vagaries, new source file GISWeatherAnalytics.py has been created and two functions weather_GIS_analytics() and invert_image() have been defined. 1172.3 Function weather_GIS_analytics() reads a Weather GIS image and computes contour and convex hull segments from it quite similar to Urban sprawl contours but computed contour polynomials are for aerial weather phenomena instead of terrains viz., cloud, wind - which is passed as parameter to weather_GIS_analytics(). 1172.4 An example Low pressure trough satellite image testlogs/Windy_WeatherGIS_2021-11-11-13-07-51.jpg over Bay of Bengal from https://www.windy.com/-Satellite-satellite?satellite,20.448,79.387,5 (unedited,unfiltered and uncomposited) has been segmented and cloud contours from it are ranked by their convex hull area and circumference. Cloud contours are labelled by integers. 1172.5 Function invert_image() inverts a thresholded image to its grayscale complement. Image inversion is particularly necessary in four color segmentation of an urban sprawl facegraph e.g. NASA VIIRS NightLights urban areas. Inverted VIIRS NightLights image indicates non-urban areas - Unlit, Rural, Underdeveloped areas and Greenery. 1172.6 Previous windy.com image has been inverted in grayscale and printed to testlogs/Windy_WeatherGIS_2021-11-11-13-07-51-inverted.jpg 1172.7 NOAA HURDAT2 Hurricane and USGS Earthquake datasets have been Sequence Mined earlier to correlate gravitational effects of celestial n-body choreographies from astronomical ephemeris datasets (NASA JPL Ephemeris) and terrestial extreme weather events. GISWeatherAnalytics supplements this Celestial-Terrestrial weather event pattern analytics. 1172.8 GIS Weather imagery could as well be a timeseries stream of images read from Streaming_AbstractGenerator Facade and yielded to GISWeatherAnalytics.py for live weather analytics - movement and velocity of cloud and wind contours, number and area of cloud-wind contours, altitude of clouds, gradual strengthening of low pressure troughs to Cyclones-Hurricanes among others. 1172.9 Image files,DOT and Logs for this commit are at: testlogs/GISWeatherAnalytics.log.11November2021 testlogs/RemoteSensingGIS/Windy_WeatherGIS_2021-11-11-13-07-51_FaceGraph.jpg testlogs/RemoteSensingGIS/Windy_WeatherGIS_2021-11-11-13-07-51_ImageNet_Keras_Theano_Segmentation_FaceGraph.dot testlogs/RemoteSensingGIS/Windy_WeatherGIS_2021-11-11-13-07-51_contour_segmented.jpg testlogs/RemoteSensingGIS/Windy_WeatherGIS_2021-11-11-13-07-51_convex_hull_segmented.jpg testlogs/RemoteSensingGIS/Windy_WeatherGIS_2021-11-11-13-07-51_segmented.jpg testlogs/Windy_WeatherGIS_2021-11-11-13-07-51-contourlabelled.jpg testlogs/Windy_WeatherGIS_2021-11-11-13-07-51-inverted.jpg testlogs/Windy_WeatherGIS_2021-11-11-13-07-51.jpg 1172.10 Various Reference Weather GIS Datasources: 1172.10.1 NOAA CPC Joint Agriculture Weather Facility - https://www.cpc.ncep.noaa.gov/products/JAWF_Monitoring/India/index.shtml - 2 weeks red-orange-yellow-green alert forecast 1172.10.2 https://www.windy.com/-Satellite-satellite?satellite,20.448,79.387,5 - Color satellite imagery and lot of layers for cloud,wind,rain,waves,air quality each of which could be a Drone obstacle avoidance parameter 1172.10.2 IMD - INSAT - https://mausam.imd.gov.in/imd_latest/contents/satellite.php - monochromatic weather satellite imagery
Srinivasan Kannan
More... almost 5 years ago
-------------------------------------------------------------------------------------------------------------------------------------------- +1171. (THEORY and FEATURE) GDP estimation from GIS Urban Sprawl Analytics - related to 694,1170 and all sections on GIS and Urban Sprawl Analytics, Merit of Large Scale Visuals, EventNet and Graphical Event Models-Causal Event Models, Computational Economics, Economic Merit, Four Color Theorem, Multiple Agent Resource Allocation, Logistic Regression, Intrinsic Merit Rankings, R Ricker and SEDAC GPW4 Population Estimations, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Chaos and Mandelbrot sets, Computational Geometry, Linear Programming - 9 November 2021 +------------------------------------------------------------------------------------------------------------------------------------------
Krishna iResearch - NeuronRain - K.Srinivasan
More... almost 5 years ago
Section 1159 updated
Krishna iResearch - NeuronRain - K.Srinivasan
More... almost 5 years ago
+--------------------------------------------------------------------------------------------------------------------------------------------- +1170. (THEORY and FEATURE) GIS Urban Sprawl Analytics - NASA VIIRS NightLights 2021 Urban Area Rankings - related to all sections on Merit of Large Scale Visuals, GIS and Urban Sprawl Analytics, Computational Geometry, Intrinsic Merit Rankings, R Ricker and SEDAC GPW4 Population Estimations, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Chaos and Mandelbrot sets, Computational Geometry, Four color theorem, Linear Programming, Economic Merit, Computational Economics - 7,8 November 2021 +---------------------------------------------------------------------------------------------------------------------------------------------
Krishna iResearch - NeuronRain - K.Srinivasan
More... almost 5 years ago
--------------------------------------------------------------------------------------------------------------------------------------------- 1170. (THEORY and FEATURE) GIS Urban Sprawl Analytics - NASA VIIRS NightLights 2021 Urban Area Rankings - related to all sections on Merit of Large Scale Visuals, GIS and Urban Sprawl Analytics, Computational Geometry, Intrinsic Merit Rankings, R Ricker and SEDAC GPW4 Population Estimations, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Chaos and Mandelbrot sets, Computational Geometry, Four color theorem, Linear Programming, Economic Merit, Computational Economics - 7,8 November 2021 --------------------------------------------------------------------------------------------------------------------------------------------- 1170.1 In continuation of Urban sprawl contour analytics of NASA VIIRS NightLights 2012 and 2016 imagery earlier, latest NASA VIIRS imagery of India dated 6 November 2021 from https://resourcewatch.org/data/explore/cli061nrt-Daily-Nighttime-lights has been analyzed by GISUrbanSprawlAnalytics.py for EMD similarity and Growth of Urbanization in India between 2016 and 2021. 1170.2 Previous image of India NightLights has been manually captured in file NightLights_13nasa-india-2021.jpg from resourcewatch.org and is not a NASA released composited and filtered image. It contains some cloud formations and lighting could be slightly exaggerated because of Deepavali fireworks on 04 November 2021 and later. Mismatch in image sizes, additional graphics could have contributed to exaggeration. 1170.3 Following are some excerpts of logs on Indian Urban area statistics in 2021 in comparison to 2016: 2021: ('Number of segments - Number of Urban areas:', 7335) 2016 to 2021: ('EMD Histogram distance similarity between images - testlogs/NightLights_13nasa-india-2016.jpg and testlogs/NightLights_13nasa-india-2021.jpg :', (0.031924109905958176, None, array([[36., 0., 0., ..., 0., 0., 0.], 1170.4 Number of Urban area circles have roughly quadrupled from 2016 to 2021 and multiplied 8 times from 2012 - 835 to 1914 (2012-2016),1914 to 7335 (2016-2021) - Exaggeration by Deepavali lights could be minimal because only luminosity might have been boosted per urban area and festival may not have created so many new contours. 1170.5 EMD similarity between 2012 and 2016 is 5% while EMD similarity between 2016 and 2021 is 3% perhaps pointing at 95% and 97% dissimilarity respectively (approximate doubling(s) in urbanization between 2012-2016 and 2016-2021). 1170.6 Previous analytics measures prima facie might hint at spectacular annual urban growth and increase in electricity consumption of 20-25% out of sync with GDP growth (which was negative in some quarters and grew on the average by 5-8% during 2012-2021 - agriculture contributes 70% to GDP of India and glaring Urban-Rural divide could be pulling down the numbers)
Srinivasan Kannan
More... almost 5 years ago
Section 1169 updated
Krishna iResearch - NeuronRain - K.Srinivasan
More... about 5 years ago
+-------------------------------------------------------------------------------------------------------------------------------------------- +1169. (THEORY and FEATURE) GIS Urban Sprawl Analytics - NASA VIIRS Nights 2012 and 2016 Distance Similarity and Urban Area Rankings - related +to all sections on Merit of Large Scale Visuals, GIS and Urban Sprawl Analytics, Computational Geometry, Intrinsic Merit Rankings, R Ricker a nd SEDAC GPW4 Population Estimations, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Chaos and Mand elbrot sets, Computational Geometry - 5 November 2021 +---------------------------------------------------------------------------------------------------------------------------------------------
Krishna iResearch - NeuronRain - K.Srinivasan
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-------------------------------------------------------------------------------------------------------------------------------------------- 1169. (THEORY and FEATURE) GIS Urban Sprawl Analytics - NASA VIIRS Nights 2012 and 2016 Distance Similarity and Urban Area Rankings - related to all sections on Merit of Large Scale Visuals, GIS and Urban Sprawl Analytics, Computational Geometry, Intrinsic Merit Rankings, R Ricker and SEDAC GPW4 Population Estimations, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Chaos and Mandelbrot sets, Computational Geometry - 5 November 2021 --------------------------------------------------------------------------------------------------------------------------------------------- 1169.1 GISUrbanSprawlAnalytics.py has been updated to compute histogram set partition distance similarity (Earth Mover Distance) measure between NASA NightLights Contour Segmented Urban Sprawl imagery of India - 2012 and 2016 - as below: ('EMD Histogram distance similarity between images - testlogs/NightLights_13nasa-india-2016.jpg and testlogs/NightLights_13nasa-india-2012.jpg :', (0.053227465599775314, None, array([[36., 0., 0., ..., 0., 0., 0.], [ 0., 34., 0., ..., 0., 0., 0.], [ 0., 0., 37., ..., 0., 0., 0.], ..., [ 0., 0., 0., ..., 25., 0., 0.], [ 0., 0., 0., ..., 1., 19., 0.], [ 0., 0., 0., ..., 0., 6., 34.]], dtype=float32))) 1169.2 Following are excerpts of logs which show more than doubling of Urban area NightLight circles between 2012 and 2016 implying an impressive urban boom and doubling of electricity consumption between 2012 and 2016 (but not in proportionate correlation to GDP growth): 2016: ('Number of segments - Number of Urban areas:', 1914) 2012: ('Number of segments - Number of Urban areas:', 835) 1169.3 2D surface of a nation is sparsely packed by closed urban area contour polynomials of arbitrary shapes. Though equal circle packing of 2D surface has been widely studied, unequal circle packing and its generic version - polynomial packing - find less or no mention. 1169.4 Following are some of Unequal Circle Packing (within a Circle or Square container) algorithms and there are no published algorithms for packing arbitrary polynomial curves within a 2D surface though Voronoi Diagram Tessellations could approximate contour polynomials by polygons: 1169.4.1 Packing Unequal Circles into a Square Container by Partitioning Narrow Action Spaces and Circle Items - https://arxiv.org/abs/1701.00541 1169.4.2 Packing unequal circles using formulation space search - https://www.sciencedirect.com/science/article/abs/pii/S0305054812002663?via%3Dihub 1169.4.3 Packing different-sized circles into a rectangular container - https://www.sciencedirect.com/science/article/abs/pii/037722179500032L 1169.5 Following are the logs,contour segment, convexhull segment, contour labelled image files generated from 2012 and 2016 VIIRS imagery for this commit: testlogs/NightLights_13nasa-india-2016-contourlabelled.jpg testlogs/NightLights_13nasa-india-2016.jpg testlogs/RemoteSensingGIS/NightLights_13nasa-india-2016_FaceGraph.jpg testlogs/RemoteSensingGIS/NightLights_13nasa-india-2016_ImageNet_Keras_Theano_Segmentation_FaceGraph.dot testlogs/RemoteSensingGIS/NightLights_13nasa-india-2016_contours_segmented.pdf testlogs/RemoteSensingGIS/NightLights_13nasa-india-2016_convex_hulls_segmented.pdf testlogs/RemoteSensingGIS/NightLights_13nasa-india-2016_segmented.jpg testlogs/GISUrbanSprawlAnalytics.log.5November2021 testlogs/NightLights_13nasa-india-2012-contourlabelled.jpg testlogs/NightLights_13nasa-india-2012.jpg testlogs/RemoteSensingGIS/NightLights_13nasa-india-2012_FaceGraph.jpg testlogs/RemoteSensingGIS/NightLights_13nasa-india-2012_ImageNet_Keras_Theano_Segmentation_FaceGraph.dot testlogs/RemoteSensingGIS/NightLights_13nasa-india-2012_contours_segmented.pdf testlogs/RemoteSensingGIS/NightLights_13nasa-india-2012_convex_hulls_segmented.pdf testlogs/RemoteSensingGIS/NightLights_13nasa-india-2012_segmented.jpg
Srinivasan Kannan
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--------------------------------------------------------------------------------------------------------------------------------------------- +1168. (THEORY and FEATURE) GIS Urban Sprawl Analytics - NASA VIIRS NightLights Urban Area Rankings - 29 October 2021 - related to all sections on Merit of Large Scale Visuals, GIS and Urban Sprawl Analytics, Computational Geometry, Intrinsic Merit Rankings, R Ricker and SEDAC GPW4 +Population Estimations, Social Network Analytics, Space filling by Curves, Circle packing, Chaos and Mandelbrot sets +---------------------------------------------------------------------------------------------------------------------------------------------
Krishna iResearch - NeuronRain - K.Srinivasan
More... about 5 years ago
--------------------------------------------------------------------------------------------------------------------------------------------- 1168. (THEORY and FEATURE) GIS Urban Sprawl Analytics - NASA VIIRS NightLights Urban Area Rankings - 29 October 2021 - related to all sections on Merit of Large Scale Visuals, GIS and Urban Sprawl Analytics, Computational Geometry, Intrinsic Merit Rankings, R Ricker and SEDAC GPW4 Population Estimations, Social Network Analytics, Space filling by Curves, Circle packing, Chaos and Mandelbrot sets --------------------------------------------------------------------------------------------------------------------------------------------- 1168.1 GISUrbanSprawlAnalytics.py has been updated to compute area of every Urban Area in contour segmented NASA VIIRS NightLights 2016 imagery by cv2.contourArea() in addition to circular area approximation, draw bounding box rectangle around each of 1825 Urban area sprawls by cv2.boundingRect(), write an id string for every urban area bounding box by cv2.putText() and sort the list of tuples of contour areas of cities which is printed as rankings of Urban areas. 1168.2 Contours of 1825 urban areas have been imprinted on VIIRS NightLight image along with their numeric id(s) through which lookup can be made on the sorted urban area ranking list of tuples. Contour id labelled image is written to testlogs/NightLights_13nasa-india-2016-contourlabelled.jpg 1168.3 Following are the affected logs and images: FaceGraph of NightLights - testlogs/RemoteSensingGIS/NightLights_13nasa-india-2016_FaceGraph.jpg 1825 Contour segmented urban sprawls of India - testlogs/RemoteSensingGIS/NightLights_13nasa-india-2016_contours_segmented.pdf 1825 Convex Hull segmented urban sprawls of India - testlogs/RemoteSensingGIS/NightLights_13nasa-india-2016_convex_hulls_segmented.pdf Python Keras and OpenCV2 Logs - testlogs/GISUrbanSprawlAnalytics.log.29October2021 NASA VIIRS India 2016 image layered by 1825 City id labelled Contours - testlogs/NightLights_13nasa-india-2016-contourlabelled.jpg 1168.4 Contours of Urban areas which are in close proximity are highly likely to merge in future. 1168.5 Contour curves fill a 2-dimensional space - either as polynomials or unequal circles. Parallels can be drawn between Fractal geometry (Koch curves which are known to be space filling - https://en.wikipedia.org/wiki/Space-filling_curve) of coastlines which are Mandelbrot sets and Contour curve geometry of urban areas - https://en.wikipedia.org/wiki/Coastline_paradox. 1168.6 Population of urban sprawls can be estimated by product of population density and contour area.
Krishna iResearch - NeuronRain - K.Srinivasan
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Updated Section 1167
Krishna iResearch - NeuronRain - K.Srinivasan
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--------------------------------------------------------------------------------------------------------------------------------------------- 1167. (THEORY and FEATURE) GIS Urban Sprawl Analytics - NASA VIIRS NightLights Urban Area and Population Estimation - implementation - 24 October 2021 - related to all sections on Merit of Large Scale Visuals, GIS and Urban Sprawl Analytics, Computational Geometry, R Ricker Population Estimator implementation, SEDAC GPW4 PopEst, Social Network Analytics --------------------------------------------------------------------------------------------------------------------------------------------- 1167.1 Because of importance of GIS geospatial imagery for Urban Sprawl Analytics, new source file GISUrbanSprawlAnalytics.py has been added to NeuronRain repositories which defines function urban_sprawl_from_segments() for reading an urban sprawl GIS image and segment it by contours and compute convex hulls around contour polynomial points. 1167.2 NASA VIIRS 2016 imagery for India NightLights python-src/image_pattern_mining/ImageNet/testlogs/NightLights_13nasa-india-2016.jpg is contour segmented by ImageGraph_Keras_Theano.py image_segmentation() and contours of 1825 urban areas in the subcontinent are read by previous function. 1167.3 Convex Hulls of 1825 urban areas are found by OpenCV2 convexHull(). Circumference of 1825 urban sprawls is computed from cv.arcLength() of the urban area segment contour polynomials and approximate urban sprawl area is derived from circumference as a circular region: Area = 3.1428 * (Circumference/2*3.1428)^2 1167.4 Segmented Contours of 1825 urban sprawls are written as JPEG image file python-src/image_pattern_mining/ImageNet/testlogs/RemoteSensingGIS/NightLights_13nasa-india-2016_segmented.jpg which are fairly accurate in demarcating urban areas and new insights of urban areas emerge - formation of megalopolises in TN,MH,Gangetic Plain and lopsided growth in certain pockets. 1167.5 Imagery and Logs for this implementation are at: Logs - ImageNet/testlogs/GISUrbanSprawlAnalytics.log.24October2021 NASA VIIRS NightLights - India 2016 - ImageNet/testlogs/NightLights_13nasa-india-2016.jpg (Copyright: https://earthobservatory.nasa.gov/features/NightLights) FaceGraph of NightLights - ImageNet/testlogs/RemoteSensingGIS/NightLights_13nasa-india-2016_FaceGraph.jpg DOT file for FaceGraph - ImageNet/testlogs/RemoteSensingGIS/NightLights_13nasa-india-2016_ImageNet_Keras_Theano_Segmentation_FaceGraph.dot 1825 Contour segmented urban sprawls of India - ImageNet/testlogs/RemoteSensingGIS/NightLights_13nasa-india-2016_contours_segmented.pdf 1825 Convex Hull segmented urban sprawls of India - ImageNet/testlogs/RemoteSensingGIS/NightLights_13nasa-india-2016_convex_hulls_segmented.pdf NASA VIIRS NightLights India 2016 image layered by 1825 urban sprawl Contour segments - ImageNet/testlogs/RemoteSensingGIS/NightLights_13nasa-india-2016_segmented.jpg 1167.6 ImageGraph_Keras_Theano.py has been updated for return value tuples of contour segments.
Srinivasan Kannan
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Section 1162 and 1165 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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Updated Requirements.txt
Krishna iResearch - NeuronRain - K.Srinivasan
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Updated Requirements.txt and BestPractices.txt
Krishna iResearch - NeuronRain - K.Srinivasan
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Sections 1165 and 1166 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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+ +-------------------------------------------------------------------------------------------------------------------------------------------- +1166. (THEORY and FEATURE) Set Partition Analytics - Complementary Set Partitions - implementation - 20 October 2021 - related to 864 and all +sections on Complement functions, Complementary sets, Set Partitions, Electronic Voting Machines, Set Partition version of Boolean Majority, +Majority Voting +-------------------------------------------------------------------------------------------------------------------------------------------- +1166.1 Streaming_SetPartitionAnalytics.py has been updated to define a new function complementary_set_partition() as per NeuronRain definition of complement set partitions (Example described in Section 864 - item 60) - each partition and its complement split an equidepth partition. +1166.2 This function iterates through parts of a partition and pads each part by zeros (numpy zero fill) to create a complementary set partition union of which together form an equidepth partition. +1166.3 Complement and Equidepth partitions for a random partition are printed. Logs for this commit are at testlogs/Streaming_SetPartitionAnalytics.Python3.9Upgrade.Complement.20October2021
Srinivasan Kannan
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--------------------------------------------------------------------------------------------------------------------------------------------- 1165. (THEORY and FEATURE) Drone Online Shopping Delivery - NeuronRainApps - Refactoring for ConvexHull and GIS analytics variables read over socket stream - related to all sections on Async IO, Drone Electronic Voting Machines, GIS and Urban Sprawl Analytics, Merit of Large Scale Visuals, Drone Obstacle Avoidance, Computational Geometry - 12 October 2021 ---------------------------------------------------------------------------------------------------------------------------------------------
Krishna iResearch - NeuronRain - K.Srinivasan
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--------------------------------------------------------------------------------------------------------------------------------------------- 1165. (THEORY and FEATURE) Drone Online Shopping Delivery - NeuronRainApps - Refactoring for ConvexHull and GIS analytics variables read over socket stream - related to all sections on Async IO, Drone Electronic Voting Machines, GIS and Urban Sprawl Analytics, Merit of Large Scale Visuals, Drone Obstacle Avoidance, Computational Geometry - 12 October 2021 --------------------------------------------------------------------------------------------------------------------------------------------- 1165.1 OnlineShoppingDelivery.py has been refactored for analytics variables read over socket stream (port 64001). Instead of inferring convex hull of the destination only from aerial GIS image by OpenCV2, optional clauses have been introduced to read longitude-latitude list of convex hull waypoints and mission way points over socket stream (nc -l 64001) port. 1165.2 GIS analytics variables have been enhanced by uncommenting earlier commented variables. Convex Hull from OpenCV is a list of integers and not in format of longitude-latitude of vertices of convex hull polygon which necessitates writing convex hull longlat(s) over socket stream or reading from file. OnlineShoppingDelivery.GISvars.txt has been updated for a reference list of variables. 1165.3 Convex Hull mission longitude-latitude list and GIS analytics key-value pair list written over socket stream have to be now demarcated by "end_longlat" and "end_gis" delimiter strings. 1165.4 start_mission() throws error "start_mission(): coro=run() done" errors if the waypoints are too faraway and PX4 console prints "waypoints are too far away" error messages. Because of this list of longitude-latitudes read over socket stream have been separated by decimals (reference: list of longitude-latitude(s) of mission.py example in MAVSDK Python source code) 1165.5 Earlier "start_mission(): coro=run() done" error has been documented also to be related to QGroundControl (QGC) in MAVSDK issues. But QGC installation is fraught with GLIBC and GLIBCXX version number errors which might require upgrade to gcc-9 and g++-9 and correct packages containing matching libstdc++.so.6 and libc.so have to be found by "strings". Some other MAVSDK documentation mention QGC is not necessary for mission. 1165.6 Earlier "coro=run() done" errors are resolved if start_mission() is populated with longlat(s) which are not faraway. 1165.7 OnlineShoppingDelivery.py is a versatile implementation which can be adapted to Drone Postal Delivery, Drone EVM, Drone Human Transport (if payload allowed is ~100kg and range of flight > 35 kms assuming solar panel powered drones), Drone Autonomous Combat (Several military drones used for precision aerial bombings have high payload), GIS Mapping Drones among others. 1165.8 Drone Human Transport (single seater) though a solution for surface traffic congestion, could cause air traffic congestion and special Drone Air Traffic Control (ATC) is necessary - ATC for drones is fully programmable (allocating non-colliding mission trajectories to drones) as drones are autopiloted and mission plans are controlled by ATC. 1165.9 Drone transport is 3-dimensional (longitude,latitude,altitude) as against 2-dimensional surface transport (longitude,latitude) and aerial transportation graph is non-planar, while surface transportation graph is planar - Open Source NASA Perseverance Mars Rover is both surface and aerial transportation (helicopter) drone - https://github.com/nasa-jpl/open-source-rover and https://opensourcerover.jpl.nasa.gov/ . 1165.10. Safe transport of items insulated from natural obstacles (e.g turbulent weather) is paramount for online shopping delivery - Drones being lightweight may not withstand ferocious onslaught of rain and winds. GIS analytics variables read over socket stream by OnlineShoppingDelivery.py Python implementation could be sourced from image and video analytics of GIS imagery by OpenCV2 - e.g list of mission longitude-latitude(s) dynamically changes based on weather vicissitudes learnt from remote sensing weather satellite photo of sky (clear,overcast,storms) which is an obstacle avoidance algorithm. 1165.11 Image analytics might require functionality to analyze any GIS imagery - SEDAC-GPW4-PopEst, Weather, Rural-Urban clusters,... - and segment those images to find facets of importance and least obstacles whose vertices have to be mapped to longitude-latitude and written over socket stream for live reading by drones (instead of nc -l). 1165.12 MAVSDK does not seem to support dynamic mission plans to add/delete/update longitude-latitude(s) during flight after upload_mission()-start_mission() and all longlat(s) must be known apriori. 1165.13 Dynamic Mission Obstacle Avoidance Algorithm by GIS imagery analytics: ----------------------------------------------------------------------------- 1165.13.1 Drone executes TAKE_PHOTO command and transmits terrestrial image back to PX4-MAVSDK ground control and satellite weather GIS photo at that instant is used for obstacle overlay - the two images have to be overlaid to filter the obstacle vertices-edges. 1165.13.2 MAVSDK Client invokes (*) transportation graph extraction algorithm on terrestrial image transmitted by drone and (*) image analytics segmentation on aerial GIS image to find segments containing obstacles - clouds, storms, dust, ... - obstacle segments are bounded by convex hulls 1165.13.3 MAVSDK client overlays the transportation graph imagery on segmented (convex hull) weather GIS imagery and masks the vertices and edges (roads and junctions) from transportation graph which coincide with obstacle segment convex hulls (e.g road-junctions vertices-edges on earth directly below and with in cloud convex hull segments in sky are masked and removed from transportation graph - in other words, holes are punctured in transportation graph fabric by convex hull obstacles) 1165.13.4 Shortest distance (Geodesic) is computed between source and destination on the obstacle masked punctured transportation graph and Geodesic longlat(s) (from Geocoding Geodesic functionality) are populated dynamically before upload_mission() and start_mission() which bypasses obstacles. 1165.14 Graph masking earlier to remove terrestrial transportation subgraphs coinciding with aerial GIS imagery obstacle segments could be non-trivial computational geometric problem. 1165.15 Obstacle segments in GIS imagery are usually convex hulls (e.g polygon ensconcing clouds) and planar intersection algorithms are required to find transportation graph edges-vertices which intersect and are with in obstacle convex hull edges-vertices of aerial GIS imagery. 1165.16 Previous algorithm could be an implementation separate from MAVSDK client which computes Graph Obstacle Masking earlier and writes thegeodesic longlat(s) over socket stream as GIS analytics variables to MAVSDK client. 1165.17 Logs for this commit are at: OnlineShoppingDelivery.py - OnlineShoppingDelivery_Drone_JMAVSIM_Flight_Simulation_Mission.log.MAVSDKClient.12October2021 nc -l 64001 - OnlineShoppingDelivery_Drone_JMAVSIM_Flight_Simulation_Mission.log.netcat.12October2021 PX4 console - OnlineShoppingDelivery_Drone_JMAVSIM_Flight_Simulation_Mission.log.PX4.12October2021 1165.18 MP4 Video of the Drone Mission flight for earlier convexhull and mission analytics variables has been uploaded at: https://kuja27.blogspot.com
Krishna iResearch - NeuronRain - K.Srinivasan
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Updated Requirements.txt
Krishna iResearch - NeuronRain - K.Srinivasan
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1156 updated - references
Krishna iResearch - NeuronRain - K.Srinivasan
More... about 5 years ago