Assessing data limitations in ML-based LCLU (Proposals Track)
Angel Encalada-Davila (ESPOL); Christian Tutiven (ESPOL University); Jose E Cordova-Garcia (ESPOL)
Abstract
This study addresses the accuracy challenge in Global Land Use and Land Cover (LULC) maps, crucial for policy making towards climate change mitigation. We evaluate two LULC products based on advanced machine learning techniques across two representative nations, Ecuador and Germany, employing a novel accuracy metric. The analysis unveils a notable accuracy enhancement in the convolutional neural network-based approach against the random forest model used for comparison. Our findings emphasize the potential of sophisticated machine learning methodologies in advancing LULC mapping accuracy, an essential stride towards data-driven, climate-relevant land management and policy decisions.