Abstract. This paper presents a multimodal deep learning framework for deforestation detection that integrates satellite-based deforestation alerts (DETER) with weather and atmospheric variables (WF). While we hypothesized that WF could provide complementary signals for short-term deforestation prediction, our experiments show that fusion models provide only isolated and modest gains, with no consistent improvement over DETER-only baselines. The WF-only model highlights structurally vulnerable regions but lacks precision in identifying which specific pixels will be deforested—an ability retained by the alert-based models. Our findings confirm the dominant role of satellite alerts for precise deforestation monitoring, with WF signals offering limited added value for operational systems.
Elezi et al. (Fri,) studied this question.