Crowd-Driven and Automated Mapping of Field Boundaries in Highly Fragmented Agricultural Landscapes of Ethiopia with Very High Spatial Resolution Imagery

16085 9 88427 2 253443 document 88427 remotesensing-11-02082.pdf application/pdf d433f0fd2134c2eaa1c16f3fa324cb93 MD5 4452255 2019-09-30 14:25:16 https://pure.iiasa.ac.at/id/eprint/16085/1/remotesensing-11-02082.pdf 16085 1 1 application/pdf text en public cc_by_4

remotesensing-11-02082.pdf

published 88428 2 253450 document 88428 lightbox.jpg image/png 2d08168ffe5a3a125b41276549390f06 MD5 47664 2019-09-30 14:35:22 https://pure.iiasa.ac.at/id/eprint/16085/2/lightbox.jpg 16085 2 2 image/png other Thumbnails conversion from text to thumbnail_lightbox en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/88427 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/88427 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/88427 archive 353 disk0/00/01/60/85 2019-09-30 14:28:10 2022-10-19 05:01:04 2019-09-30 14:28:10 article show Marshall M. Crommelinck S. Kohli D. Perger C. 1899 Yang M.Y. Ghosh A. Fritz S. 8130 0000-0003-0420-8549 Bie K. Nelson A. Crowd-Driven and Automated Mapping of Field Boundaries in Highly Fragmented Agricultural Landscapes of Ethiopia with Very High Spatial Resolution Imagery pub prog_esm agriculture; cropland; food security; image segmentation; object detection; crowdsourcing; remote sensing; WorldView Mapping the extent and location of field boundaries is critical to food security analysis but remains problematic in the Global South where such information is needed the most. The difficulty is due primarily to fragmentation in the landscape, small farm sizes, and irregular farm boundaries. Very high-resolution satellite imagery affords an opportunity to delineate such fields, but the challenge remains of determining such boundaries in a systematic and accurate way. In this paper, we compare a new crowd-driven manual digitization tool (Crop Land Extent) with two semi-automated methods (contour detection and multi-resolution segmentation) to determine farm boundaries from WorldView imagery in highly fragmented agricultural landscapes of Ethiopia. More than 7000 one square-kilometer image tiles were used for the analysis. The three methods were assessed using quantitative completeness and spatial correctness. Contour detection tended to under-segment when compared to manual digitization, resulting in better performance for larger (approaching 1 ha) sized fields. Multi-resolution segmentation on the other hand, tended to over-segment, resulting in better performance for small fields. Neither semi-automated method in their current realizations however are suitable for field boundary mapping in highly fragmented landscapes. Crowd-driven manual digitization is promising, but requires more oversight, quality control, and training than the current workflow could allow. 2019-09-05 published Molecular Diversity Preservation International (MDPI) 10.3390/rs11182082 228 98 public Remote Sensing 11 18 e2082 TRUE 2072-4292 Harnessing the power of crowdsourcing to improve land cover and land-use information (CROWDLAND, FP7 617754) FALSE yes info:eu-repo/grantAgreement/EC/FP7/617754/EU//CROWDLAND info:eu-repo/semantics/article info:eu-repo/semantics/openAccess