# MultiEarth 2023 Deforestation Challenge -- Team FOREVER

arXiv: https://arxiv.org/abs/2306.11762 · HTML: https://mlsanity.com/paper/2306.11762/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 20 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [MultiEarth 2023 -- Multimodal Learning for Earth and Environment Workshop and Challenge](https://mlsanity.com/paper/2306.04738/) (2023)
- [MultiEarth 2022 Deforestation Challenge -- ForestGump](https://mlsanity.com/paper/2206.10831/) (2022)

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

- [Segmentation of arbitrary features in very high resolution remote sensing imagery](https://mlsanity.com/paper/2412.16046/) (2024)

## Abstract (arXiv)

It is important problem to accurately estimate deforestation of satellite imagery since this approach can analyse extensive area without direct human access. However, it is not simple problem because of difficulty in observing the clear ground surface due to extensive cloud cover during long rainy season. In this paper, we present a multi-view learning strategy to predict deforestation status in the Amazon rainforest area with latest deep neural network models. Multi-modal dataset consists of three types of different satellites imagery, Sentinel-1, Sentinel-2 and Landsat 8 is utilized to train and predict deforestation status. MMsegmentation framework is selected to apply comprehensive data augmentation and diverse networks. The proposed method effectively and accurately predicts the deforestation status of new queries.