# Forestry digital twin with machine learning in Landsat 7 data

arXiv: https://arxiv.org/abs/2204.01709 · HTML: https://mlsanity.com/paper/2204.01709/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 2 Apr 2022.

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

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Adversarial Autoencoders](https://mlsanity.com/paper/1511.05644/) (2015)

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

- [Miti360: A Comprehensive Dataset for Improved Reforestation Monitoring](https://mlsanity.com/paper/2606.29447/) (2026)

## Abstract (arXiv)

Modeling forests using historical data allows for more accurately evolution analysis, thus providing an important basis for other studies. As a recognized and effective tool, remote sensing plays an important role in forestry analysis. We can use it to derive information about the forest, including tree type, coverage and canopy density. There are many forest time series modeling studies using statistic values, but few using remote sensing images. Image prediction digital twin is an implementation of digital twin, which aims to predict future images bases on historical data. In this paper, we propose an LSTM-based digital twin approach for forest modeling, using Landsat 7 remote sensing image within 20 years. The experimental results show that the prediction twin method in this paper can effectively predict the future images of study area.