# BUDD: Multi-modal Bayesian Updating Deforestation Detections

arXiv: https://arxiv.org/abs/2001.10661 · HTML: https://mlsanity.com/paper/2001.10661/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2020.

## Built on by (most cited first, 1 of 1)

- [Scientific Data Compression and Super-Resolution Sampling](https://mlsanity.com/paper/2511.13675/) (2025)

## Abstract (arXiv)

The global phenomenon of forest degradation is a pressing issue with severe implications for climate stability and biodiversity protection. In this work we generate Bayesian updating deforestation detection (BUDD) algorithms by incorporating Sentinel-1 backscatter and interferometric coherence with Sentinel-2 normalized vegetation index data. We show that the algorithm provides good performance in validation AOIs. We compare the effectiveness of different combinations of the three data modalities as inputs into the BUDD algorithm and compare against existing benchmarks based on optical imagery.