Annotating Satellite Images of Forests with Keywords from a Specialized Corpus in the Context of Change Detection

• URL: http://arxiv.org/abs/2509.13586v1
• Date: Tue, 16 Sep 2025 23:00:16 GMT
• Title: Annotating Satellite Images of Forests with Keywords from a Specialized Corpus in the Context of Change Detection
• Authors: Nathalie Neptune, Josiane Mothe,
• Abstract summary: We present a method for detecting deforestation in the Amazon using image pairs from Earth observation satellites.<n>Our method leverages deep learning techniques to compare the images of the same area at different dates and identify changes in the forest cover.<n>We also propose a visual semantic model that automatically annotates the detected changes with relevant keywords.
• Score: 2.5782420501870296
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Amazon rain forest is a vital ecosystem that plays a crucial role in regulating the Earth's climate and providing habitat for countless species. Deforestation in the Amazon is a major concern as it has a significant impact on global carbon emissions and biodiversity. In this paper, we present a method for detecting deforestation in the Amazon using image pairs from Earth observation satellites. Our method leverages deep learning techniques to compare the images of the same area at different dates and identify changes in the forest cover. We also propose a visual semantic model that automatically annotates the detected changes with relevant keywords. The candidate annotation for images are extracted from scientific documents related to the Amazon region. We evaluate our approach on a dataset of Amazon image pairs and demonstrate its effectiveness in detecting deforestation and generating relevant annotations. Our method provides a useful tool for monitoring and studying the impact of deforestation in the Amazon. While we focus on environment applications of our work by using images of deforestation in the Amazon rain forest to demonstrate the effectiveness of our proposed approach, it is generic enough to be applied to other domains.

Related papers

• Identification of Deforestation Areas in the Amazon Rainforest Using Change Detection Models [1.2891210250935148]
  Satellite-based Monitoring Project of Deforestation in the Brazilian Legal Amazon (PRODES) is a project of the National Institute for Space Research (INPE)<n>Recently, machine learning models have been developed using PRODES data to support this effort.<n>We evaluate various change detection models in a unified dataset, including fully convolutional models and networks incorporating self-attention mechanisms.
  arXiv  Detail & Related papers  (2025-12-08T22:18:19Z)
• Data Augmentation and Resolution Enhancement using GANs and Diffusion Models for Tree Segmentation [49.13393683126712]
  Urban forests play a key role in enhancing environmental quality and supporting biodiversity in cities.<n> accurately detecting trees is challenging due to complex landscapes and the variability in image resolution caused by different satellite sensors or UAV flight altitudes.<n>We propose a novel pipeline that integrates domain adaptation with GANs and Diffusion models to enhance the quality of low-resolution aerial images.
  arXiv  Detail & Related papers  (2025-05-21T03:57:10Z)
• How to Identify Good Superpixels for Deforestation Detection on Tropical Rainforests [0.0]
  We evaluate 16 superpixel methods in satellite images to support a deforestation detection system in tropical forests. According to our results, ERS, GMMSP, and DISF perform best on UE, BR, and SIRS, respectively. Superpixel methods with better trade-offs between delineation, homogeneity, compactness, and regularity are more suitable for identifying good superpixels for deforestation detection tasks.
  arXiv  Detail & Related papers  (2024-09-06T15:05:32Z)
• Rapid Deforestation and Burned Area Detection using Deep Multimodal Learning on Satellite Imagery [3.8073142980733]
  Deforestation estimation and fire detection in the Amazon forest poses a significant challenge due to the vast size of the area. multimodal satellite imagery and remote sensing offer a promising solution for estimating deforestation and detecting wildfire in the Amazonia region. This research paper introduces a new curated dataset and a deep learning-based approach to solve these problems using convolutional neural networks (CNNs) and comprehensive data processing techniques.
  arXiv  Detail & Related papers  (2023-07-10T21:49:30Z)
• Neuroevolution-based Classifiers for Deforestation Detection in Tropical Forests [62.997667081978825]
  Millions of hectares of tropical forests are lost every year due to deforestation or degradation. Monitoring and deforestation detection programs are in use, in addition to public policies for the prevention and punishment of criminals. This paper proposes the use of pattern classifiers based on neuroevolution technique (NEAT) in tropical forest deforestation detection tasks.
  arXiv  Detail & Related papers  (2022-08-23T16:04:12Z)
• MultiEarth 2022 Deforestation Challenge -- ForestGump [0.0]
  We present an accurate deforestation estimation method with conventional UNet and comprehensive data processing. The diverse channels of Sentinel-1, Sentinel-2 and Landsat 8 are carefully selected and utilized to train deep neural networks. With the proposed method, deforestation status for novel queries are successfully estimated with high accuracy.
  arXiv  Detail & Related papers  (2022-06-22T04:10:07Z)
• Multiple-environment Self-adaptive Network for Aerial-view Geo-localization [85.52750931345287]
  Aerial-view geo-localization tends to determine an unknown position through matching the drone-view image with the geo-tagged satellite-view image. We propose a Multiple-environment Self-adaptive Network (MuSe-Net) to adjust the domain shift caused by environmental changing. In particular, MuSe-Net employs a two-branch neural network containing one multiple-environment style extraction network and one self-adaptive feature extraction network.
  arXiv  Detail & Related papers  (2022-04-18T16:04:29Z)
• Texture based Prototypical Network for Few-Shot Semantic Segmentation of Forest Cover: Generalizing for Different Geographical Regions [0.0]
  The proposed approach is trained for identifying tropical forests of South Asia and adapted to determine the temperate forest of Central Europe. An IoU of 0.62 for forest class (1-way 1-shot) was obtained using the proposed method, which is significantly higher (0.46 for PANet) than the existing few-shot semantic segmentation approach.
  arXiv  Detail & Related papers  (2022-03-29T15:48:17Z)
• Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Bayesian Deep Learning [74.94436509364554]
  We propose a Bayesian deep learning approach to densely estimate forest structure variables at country-scale with 10-meter resolution. Our method jointly transforms Sentinel-2 optical images and Sentinel-1 synthetic aperture radar images into maps of five different forest structure variables. We train and test our model on reference data from 41 airborne laser scanning missions across Norway.
  arXiv  Detail & Related papers  (2021-11-25T16:21:28Z)
• Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection [60.83360138070649]
  The paper presents an approach for analyzing aerial images of a potato crop using deep neural networks. The main objective is to demonstrate automated spatial recognition of a healthy versus stressed crop at a plant level. Experimental validation demonstrated the ability for distinguishing healthy and stressed plants in field images, achieving an average Dice coefficient of 0.74.
  arXiv  Detail & Related papers  (2021-06-14T21:57:40Z)
• Counting Cows: Tracking Illegal Cattle Ranching From High-Resolution Satellite Imagery [59.32805936205217]
  Cattle farming is responsible for 8.8% of greenhouse gas emissions worldwide. We obtained satellite imagery of the Amazon at 40cm resolution, and compiled a dataset of 903 images containing a total of 28498 cattle. Our experiments show promising results and highlight important directions for the next steps on both counting algorithms and the data collection process for solving such challenges.
  arXiv  Detail & Related papers  (2020-11-14T19:07:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.