# Automatic deforestation detectors based on frequentist statistics and their extensions for other spatial objects

arXiv: https://arxiv.org/abs/2112.01063 · HTML: https://mlsanity.com/paper/2112.01063/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 41 references that are in the map.
- First posted on arXiv 2 Dec 2021.

## Builds on (most cited first, 1 of 1)

- [Estimating Atmospheric Motion Winds from Satellite Image Data using Space-time Drift Models](https://mlsanity.com/paper/1902.09653/) (2019)

## Abstract (arXiv)

This paper is devoted to the problem of detection of forest and non-forest areas on Earth images. We propose two statistical methods to tackle this problem: one based on multiple hypothesis testing with parametric distribution families, another one -- on non-parametric tests. The parametric approach is novel in the literature and relevant to a larger class of problems -- detection of natural objects, as well as anomaly detection. We develop mathematical background for each of the two methods, build self-sufficient detection algorithms using them and discuss practical aspects of their implementation. We also compare our algorithms with those from standard machine learning using satellite data.