Integration of Machine Learning and Open Access Geospatial Data for Land Cover Mapping

Abstract

In-time and accurate monitoring of land cover and land use are essential tools for countries to achieve sustainable food production. However, many developing countries are struggling to efficiently monitor land resources due to the lack of financial support and limited access to adequate technology. This study aims at offering a solution to fill in such a gap in developing countries, by developing a land cover solution that is free of costs. A fully automated framework for land cover mapping was developed using 10-m resolution open access satellite images and machine learning (ML) techniques for the African country of Lesotho. Sentinel-2 satellite images were accessed through Google Earth Engine (GEE) for initial processing and feature extraction at a national level. Also, Food and Agriculture Organization’s land cover of Lesotho (FAO LCL) data were used to train a support vector machine (SVM) and bagged trees (BT) classifiers. SVM successfully classified urban and agricultural lands with 62 and 67% accuracy, respectively. Also, BT could classify the two categories with 81 and 65% accuracy, correspondingly. The trained models could provide precise LC maps in minutes or hours. they can also be utilized as a viable solution for developing countries as an alternative to traditional geographic information system (GIS) methods, which are often labor intensive, require acquisition of very high-resolution commercial satellite imagery, time consuming and call for high budgets.

Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation

“…The GEE environment integrates several different classifiers. We compared the performance of three of them, chosen according to their wide use and reliability in LC classification [11,14,86,[97][98][99][100][101]: random forest (RF), classification and regression tree (CART), and support vector machine (SVM).…”

Flood Hazard Mapping Using the Flood and Flash-Flood Potential Index in the Buzău River Catchment, Romania

“…The database contains the locations of 168 historical floods (Figure 2a) which were obtained from the National Administration "Romanian Waters" and 172 locations affected by torrentiality ( Figure 2b). According to previous studies which use Machine Learning techniques [45][46][47][48][49][50], the training and testing data were split in a 70% ratio for the training samples and 30% for the testing samples. This step is important as the proposed models are trained on the training samples while the testing…”

Google Earth Engine: Application of Algorithms for Remote Sensing of Crops in Tuscany (Italy)

“…Recently Google Earth Engine (GEE) has been used on a wide range of Earth observation activities as i) land cover mapping of Continental Africa by integrating pixel-based and object-based algorithms using S2 and L8 data (Xiong et al, 2017), ii) paddy rice mapping in north eastern Asia using L8 images, (Dong et al, 2016), iii) deriving cropland extent product of Australia and China (Teluguntla et al, 2018), iv) evaluating combinations of temporally aggregated S1, S2 and L8 for land cover mapping (Carrasco et al, 2019), v) testing the performances of S1 data for classifying croplands (Mirelva, Nagasawa, 2019), vi) land cover classification in Lesotho using machine learning and S2 data (Mardani et al, 2019).…”