Our PoolNet model, available to users of GBDX, identifies properties with swimming pools quickly and accurately using satellite imagery. PoolNet employs a convolutional neural network that harnesses the power, flexibility, and efficiency of deep learning.
Using multispectral data which can see many types of materials by their unique spectral "fingerprints," you can locate and identify materials that would be impossible identify with just the human eye. With GBDX, DigitalGlobe’s cloud-based platform for geospatial big data, you cost-effectively do this over many or very large geographic areas.
James Crawford, Founder and CEO of Orbital Insight, has been interested in space for a long time. When he worked as robotics and artificial intelligence expert for NASA, Crawford pioneered AI support for spacecraft and observation satellites. Now, he has turned his attention to planet Earth, using machine learning to extract intelligence from Geospatial [...]
What is the best way to see our changing planet? It depends--what do you need to do with the image? What question do you need answered? Thousands of satellites, airplanes, helicopters, and drones collect images of our planet every day. We make sense of all these sensors by benchmarking certain capabilities. To provide the optimal [...]
Developers looking for a clean, robust web map library that can reference data and services from Open Source geo-stacks (like PostGIS and GeoServer) typically implement OpenLayers because of it's vast library of integration examples, mobile integration and massive FAQ database via StackOverflow and GIS StackExchange networks. With DigitalGlobe Maps API, the integration with OpenLayers is truly seamless and simple. [...]