Tree Cover Estimation in Global Drylands from Space Using Deep Learning Guirado, Emilio Alcaraz Segura, Domingo Cabello, Javier Puertas-Ruíz, Sergio Herrera Triguero, Francisco Tabik, Siham Convolutional neural networks Data augmentation Deep learning Dry forest Forest mapping Large-scale datasets Transfer learning We are grateful to Javier Montes for providing help with the Python scripts to access Google Maps’ API. Accurate tree cover mapping is of paramount importance in many fields, from biodiversity conservation to carbon stock estimation, ecohydrology, erosion control, or Earth system modelling. Despite this importance, there is still uncertainty about global forest cover, particularly in drylands. Recently, the Food and Agriculture Organization of the United Nations (FAO) conducted a costly global assessment of dryland forest cover through the visual interpretation of orthoimages using the Collect Earth software, involving hundreds of operators from around the world. Our study proposes a new automatic method for estimating tree cover using artificial intelligence and free orthoimages. Our results show that our tree cover classification model, based on convolutional neural networks (CNN), is 23% more accurate than the manual visual interpretation used by FAO, reaching up to 79% overall accuracy. The smallest differences between the two methods occurred in the driest regions, but disagreement increased with the percentage of tree cover. The application of CNNs could be used to improve and reduce the cost of tree cover maps from the local to the global scale, with broad implications for research and management. 2020-03-25T13:06:48Z 2020-03-25T13:06:48Z 2020-01-21 info:eu-repo/semantics/article Guirado, E.; Alcaraz-Segura, D.; Cabello, J.; Puertas-Ruíz, S.; Herrera, F.; Tabik, S. Tree Cover Estimation in Global Drylands from Space Using Deep Learning. Remote Sens. 2020, 12, 343. [doi:10.3390/rs12030343] http://hdl.handle.net/10481/60633 10.3390/rs12030343 eng EC/H2020/641762 http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España MDPI