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Multimodal Fusion for Deforestation Detection: Integrating Weather and Satellite Alerts with Deep Learning

Year: 2026

Type: article

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.

Source: ISPRS annals of the photogrammetry, remote sensing and spatial information sciences

DOI: 10.5194/isprs-annals-x-3-w4-2025-121-2026

Authors: Kevin Elezi, João Pedro Martinez, Felipe Ferrari, Raul Queiroz Feitosa

Institutions: Politecnico di Torino, Pontifícia Universidade Católica do Rio de Janeiro, Military Institute of Engineering

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

SDGs: Life on land, Climate action

Open Access status: diamond

OpenAlex ID: W7135188237