02413nam a2200229 a 450000100080000000500110000800800410001902400380006010000170009824501340011526002860024950005780053552008990111365300240201265300240203665300190206065300300207970000160210970000240212570000140214970000200216310614242021-04-09       2020    bl uuuu     u0uu1 u    #d7 a10.1007/978-3-030-58811-3_522DOI1 aHASTINGS, F.  aLand-cover mapping of agricultural areas using machine learning in Google Earth engine. (Conference paper)h[electronic resource]  aIn: Gervasi O. et al. (eds) Computational Science and Its Applications - ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science, vol 12252. International Conference on Computational Science and Its Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-58811-3_52c1007  aArticle history:  First Online 29 September 2020. Volume Editors: Gervasi O.,Murgante B.,Misra S. .,Garau C.,Blecic I.,Taniar D.,Apduhan B.O.,Rocha A.M.A.C.,Tarantino E.,Torre C.M.,Karaca Y. Publisher: Springer Science and Business Media Deutschland GmbH. 20th International Conference on Computational Science and Its Applications, ICCSA 2020; Cagliari; Italy; 1 July 2020 through 4 July 2020; Code 249529. Corresponding author: Hastings, F.; School of Agronomy, Universidad de la República, Av. Gral. Eugenio Garzón 780, Montevideo, Uruguay; email:fhastings@mgap.gub.uy  aLand-cover mapping is critically needed in land-use planning and policy making. Compared to other techniques, Google Earth Engine (GEE) offers a free cloud of satellite information and high computation capabilities. In this context, this article examines machine learning with GEE for land-cover mapping. For this purpose, a five-phase procedure is applied: (1) imagery selection and pre-processing, (2) selection of the classes and training samples, (3) classification process, (4) post-classification, and (5) validation. The study region is located in the San Salvador basin (Uruguay), which is under agricultural intensification. As a result, the 1990 land-cover map of the San Salvador basin is produced. The new map shows good agreements with past agriculture census and reveals the transformation of grassland to cropland in the period 1990?2018. © 2020, Springer Nature Switzerland AG.  aAgricultural region  aGoogle earth engine  aLand-cover map  aSupervised classification1 aFUENTES, I.1 aPÉREZ-BIDEGAIN, M.1 aNAVAS, R.1 aGORGOGLIONE, A.