Colombia is a key actor related with the adoption of the relevant UNFCC provisions referred in the context of obtaining result-based payments for REDD+ actions. Since 2012, the Colombian Forest Monitoring System (SMByC, Spanish acronym) has generated wall-to-wall historical information about the deforestation process over the last 27 years at national and regional levels. The SMByC generates a methodology that integrates tools for the preprocessing and semi-automated processing of satellite imagery to detect and quantify the loss of forest cover by deforestation. Also, the SMByC has done an extensive literature review, collecting qualitative and quantitative information to identify how the drivers of deforestation (illicit crops, illegal mining and postconflict scenario) could result in an increased forest lost during a postconflict scenario. Given that Colombia is a country conformed by regions that have differentially socioeconomic and environmental conditions, the study shows how the methods could generate official information and specifies the context of deforestation process of those regions.

The processes of change detection (CD) in land use and land cover through satellite images acquired in different temporal phases represent a key process to monitor the land, the environment and evaluate disasters. Currently the methodologies used for CD based on pixels do not allow processing a large amount of images massively, so it is not possible to analyze all the available information. Main aim of this research is to evaluate the performance of Deep learning methods for CD, grouped in two approaches, across open mining areas from PlanetScope (PS) image time series of the Cerrejon mine in Colombia. Two approaches for change generation are proposed, one based on post-classification comparison in which two convolutional neural network (CNN) architectures are evaluated: U-Net and Feature Pyramidal Network (FPN) for the classification of mining areas along the time series, for this purpose different models with different hyperparameters were generated and trained to select the most suitable for such process and subsequently perform the difference between the periods of the series; and a second approach based on direct change detection in which a modified U-Net network was evaluated from pairs of images. For this purpose, different models were also trained selecting the most appropriate for the detection of changes for each period of the time series, obtaining a map of changes for each approach, each one the results were validated; the most appropriate approach was number 2 (Direct change detection), with kappa accuracies greater than 0.9 in each period of the time series.

The generation of reliable and up-to-date national land cover information is essential for environmental management, climate action, and territorial planning. In Colombia, the CORINE Land Cover Colombia (CLCC) framework has been the official reference for land cover monitoring since 2000, traditionally updated through expert-based Computer-Assisted PhotoInterpretation (CAPI) at a 1:100,000 scale. However, increasing demands for higher spatial resolution and more frequent temporal updates have made process optimization necessary, driving the incorporation of cloud-based processing and artificial intelligence (AI), including machine learning and deep learning algorithms. This study presents a semi-automated methodology for generating a new generation of harmonized CLCC-compatible raster land cover maps at a 1:50,000 scale—offering four times greater spatial detail than the official vector product—with the capacity for semi-automated annual updates. The approach combines legend harmonization from 55 to 23 classes, historical CORINE Land Cover (CLC) polygon-guided sample generation, spectral stability analysis, and regionalized classification across 190 homogeneous subregions, supported by a reproducible cloud-based architecture. National land cover maps were produced for 2020, 2022, and 2024 with thematic accuracies above 80% and Kappa coefficients up to 0.87, alongside change maps for the 2022–2024 period capturing key dynamics in agricultural frontier expansion, wetland variability, and urban expansion. The resulting products also provide structured inputs for expert-based CAPI workflows, supporting the continuous updating of the official 1:100,000 CLCC map. The results demonstrate the operational capacity of integrating AI, cloud computing, and expert knowledge to strengthen Colombia’s national land cover monitoring system.