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Accurate and topical spatial datasets representing human populations are foundational to solving humanitarian issues. This paper provides unique solutions to accurately map human settlements both at scale and across remote areas. A method of village boundary extraction from very high resolution optical satellite imagery is proposed. Furthermore, the supplement of a crowd-sourced validation process to filter the detections for higher accuracy and the automated mosaic techniques are detailed. To demonstrate the computational and informational scalability of the process, four distinct geographic locations in Nigeria, Somalia, Pakistan, and Afghanistan were analyzed for a total processed area of 592 000 km 2 , comprised of 1159 high-resolution DigitalGlobe images. The geographic variability of the locations and the scale of the projects required dynamic and automated solutions, respectively. The curated results exhibit high recall and precision of human settlement data in remote as well as urban areas. Crowdsourced validation allows complete control over the precision of the final village boundary layer, and given time, an effective 100% precision can be achieved. This highly scalable and precise system is perfectly adequate for processing regional and country-scale areas, with minimal human effort.

A method for analyzing the urbanization process from multitemporal SPOT 5 panchromatic images is presented. The analysis is performed by unsupervised change detection between built-up presence indicators extracted separately from the scenes. The obtained index has an effective resolution which is coarser than the Ground Sample Distance, thus allowing a good spatial match between the indicators. Then, a local Mixed Information change indicator is employed in order to capture the non-linear temporal behaviors. A region-based approach is developed in synergy with the local Mixed Information in order to process consistently large scenes which may be affected by regional acquisition distortions. Experiments are conducted on SPOT 5 scenes acquired in 2003 and 2008 which cover a suburban area (45×45 km2) of the city of Tangshan in China. The urbanization detector is validated by visual interpretation, giving an equal omission/commission probability of 10%. A comparison to linear change indicators highlights an improvement of 15% of the equal omission/commission probability.

A general framework for processing high and very-high resolution imagery in support of a Global Human Settlement Layer (GHSL) is presented together with a discussion on the results of the first operational test of the production workflow. The test involved the mapping of 24.3 million km2 of the Earth surface spread in four continents, corresponding to an estimated population of 1.3 billion people in 2010. The resolution of the input image data ranges from 0.5 to 10 meters, collected by a heterogeneous set of platforms including satellite SPOT (2 and 5), CBERS 2B, RapidEye (2 and 4), WorldView (1 and 2), GeoEye 1, QuickBird 2, Ikonos 2, and airborne sensors. Several imaging modes were tested including panchromatic, multispectral and pan-sharpened images. A new fully automatic image information extraction, generalization and mosaic workflow is presented that is based on multiscale textural and morphological image features extraction. New image feature compression and optimization are introduced, together with new learning and classification techniques allowing for the processing of HR/VHR image data using low-resolution thematic layers as reference. A new systematic approach for quality control and validation allowing global spatial and thematic consistency checking is proposed and applied. The quality of the results are discussed by sensor, band, resolution, and eco-regions. Critical points, lessons learned and next steps are highlighted.