Antananarivo - 2022 Land cover map

We publish two land cover maps of the city of Antananarivo produced with data acquired in 2022 using a methodology combining machine learning and object-based image analysis (OBIA). This work follows on from work that resulted in a map of the same area in 2017. The maps are produced by processing satellite images using the Moringa processing chain developed in our lab. We use a Pleiades very high spatial resolution (VHSR) image, a time series of Sentinel-2 images, a digital terrain model (DTM) and a reference database. <br> The Pleiades image is used to generate a layer of objects using a segmentation algorithm. Each object is then classified using the variables from the THRS image, the time series and the DTM information. The hierarchical nomenclature used consists of four levels with a number of classes ranging from 4 to 19. We only publish here the most detailed map (level 4) which contains, however, in the attribute table, the information of the other levels. <br> The overall accuracy of the maps ranges from 93% to 84%. Such land cover products are very rare in Madagascar, so we decided to make them open access so that they can be used by land managers and researchers. <br> <b>Warning, since December 2, 2022 we publish a new version to limit the effects related to flooding (the wetland class was overestimated on the previous version).</b> <ul> <li>The map entitled "version 1" is produced with the SRTM digital surface model (DSM) with a spatial resolution of 30m.</li> <li>The map entitled "version 2" is produced with a DTM and a LiDAR DSM with a spatial resolution of 1m (over a reduced area)</li></ul> <b>A technical report describing the method implemented and the statistics obtained at the validation step is available here : <a href="https://agritrop.cirad.fr/602680"> https://agritrop.cirad.fr/602680 </a></b>

Bibliographic Details

Main Authors:	Dupuy, Stéphane, Andriamanga, Andoniaina Valérie, Gaetano, Raffaele, Burnod, Perrine
Format:	Dataset biblioteca
Language:	English French
Published:	CIRAD Dataverse 2022
Subjects:	Agricultural Sciences, Computer and Information Science, Earth and Environmental Sciences, télédétection, environnement, Pleiades, Sentinel-2, environment, spatial database, base de données spatiale, remote sensing, lidar,
Online Access:	https://doi.org/10.18167/DVN1/RE1MDM
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