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Location Intelligence from Imagery
- 1. Location Intelligence From Imagery Geospatial Intelligence for Local & Hyperlocal Spaces #LetsTalkDeepTech Webcast Ujaval Gandhi Founder, Spatial Thoughts ujaval@spatialthoughts.com
- 2. Types of Geospatial Imagery Satellite Imagery High resolution imagery from satellite constellations allow continuous monitoring of large areas Drone Imagery UAV Platforms allow cost effective and on- demand data capture for local information Street-level Imagery Smartphones, Dashcams, 360° cameras take geotagged panoramas for capturing hyperlocal data
- 3. Deriving Intelligence From Imagery Feature Extraction Detect and extract features such as cars, roads, buildings, infrastructure assets Change Detection Determine temporal change to urban environment and infrastructure projects Monitoring Regular monitoring of assets and generating insights
- 4. Demo: Feature Extraction Descartes Labs GeoVisual Search A 50-layer ResNet built with Keras and pre-trained on ImageNet, fine- tuned to classify into approximately 100 OpenStreetMap (OSM) classes, like parking lots or golf courses https://medium.com/descarteslabs-team/geovisual-search-using-computer-vision-to-explore-the-earth-275d970c60cf
- 6. OpenStreetMap A case study on mapping in the modern age with imagery intelligence ‘Wikipedia’ for map data A free and open editable map for the whole world. Created by volunteer mappers assisted by machine-generated data. A Living Map: 5 million changes/day https://wiki.openstreetmap.org/wiki/About_OpenStreetMap https://osmstats.neis-one.org/?item=changesets
- 7. Building Footprints by Microsoft Microsoft trained a DNN to extract building geometry and ran it on high resolution imagery from Bing Maps. Open Dataset of 125M building footprints in the US, 12M in Canada and 18M in Uganda/Tanzania Much of it has been imported to OpenStreetMap Image Courtesy: Microsoft https://github.com/microsoft/USBuildingFootprints
- 8. Maps with AI by Facebook Facebook has built a service to generate road geometries from Maxar’s high resolution imagery. Used deep learning and weakly supervised training techniques. Key insight was to generate noisy, imperfect training data that allowed for differences between roads across the globe. Human mappers review, fix validation checks and add those to OpenStreetMap Image Courtesy: Facebook DeepGlobe model draws non-existent roads Global OSM Model performs well https://ai.facebook.com/blog/mapping-roads-through-deep-learning-and-weakly-supervised-training/
- 9. Street Level Imagery by Mapillary Provides tools to capture street level imagery. Vision based algorithms for ● Traffic sign recognition ● Semantic segmentation Integrates with OpenStreetMap to assist mappers with global imagery coverage Image Courtesy: Mapillary https://wiki.openstreetmap.org/wiki/Mapillary
- 10. Questions? For more geospatial industry insights, subscribe to my free monthly newsletter bit.ly/spatialthoughts-newsletter
Hinweis der Redaktion
- https://search.descarteslabs.com/?layer=naip_v2_rgb_2014-2015#lat=39.2322531&lng=-100.8544921&skipTut=true&zoom=5
- https://github.com/microsoft/USBuildingFootprints, https://github.com/microsoft/CanadianBuildingFootprints, https://github.com/microsoft/Uganda-Tanzania-Building-Footprints # Node: ResNet-34 with ImageNet -- 34 layers plain ResNet for image classification # Reference: "Deep Residual Learning for Image Recognition" https://arxiv.org/abs/1512.03385
- https://github.com/facebookmicrosites/Open-Mapping-At-Facebook/wiki/FAQ https://research.fb.com/publications/building-high-resolution-maps-for-humanitarian-aid-and-development-with-weakly-and-semi-supervised-learning/