ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery

用于印度尼西亚油棕测绘的GeoAI基础模型开放基准数据集

An Open Benchmark Dataset for GeoAI Foundation Models for Oil Palm Mapping in Indonesia

针对印度尼西亚油棕种植导致的森林砍伐问题，构建了基于2020-2024年高分辨率卫星影像专家标注的开放地理空间数据集，提供多边形全覆盖标注与层级分类，适用于训练和基准测试GeoAI基础模型，助力可持续监测。

Multimodal SuperCon: Classifier for Drivers of Deforestation in Indonesia

Deforestation is one of the contributing factors to climate change. Climate change has a serious impact on human life, and it occurs due to emission of greenhouse gases, such as carbon dioxide, to the atmosphere. It is important to know the causes of deforestation for mitigation efforts, but there is a lack of data-driven research studies to predict these deforestation drivers. In this work, we propose a contrastive learning architecture, called Multimodal SuperCon, for classifying drivers of deforestation in Indonesia using satellite images obtained from Landsat 8. Multimodal SuperCon is an a…

TreeFormers -- 探索用于森林砍伐驱动因素分类的视觉变换器

TreeFormers -- An Exploration of Vision Transformers for Deforestation Driver Classification

本文提出利用视觉变换器对印度尼西亚森林卫星图像进行森林砍伐驱动因素分类，通过微调预训练ViT及数据增强等技术，实现72.9%的测试准确率，显著优于基线模型。

High carbon stock mapping at large scale with optical satellite imagery and spaceborne LIDAR

The increasing demand for commodities is leading to changes in land use worldwide. In the tropics, deforestation, which causes high carbon emissions and threatens biodiversity, is often linked to agricultural expansion. While the need for deforestation-free global supply chains is widely recognized, making progress in practice remains a challenge. Here, we propose an automated approach that aims to support conservation and sustainable land use planning decisions by mapping tropical landscapes at large scale and high spatial resolution following the High Carbon Stock (HCS) approach. A deep lear…

Predicting Forest Fire Using Remote Sensing Data And Machine Learning

Over the last few decades, deforestation and climate change have caused increasing number of forest fires. In Southeast Asia, Indonesia has been the most affected country by tropical peatland forest fires. These fires have a significant impact on the climate resulting in extensive health, social and economic issues. Existing forest fire prediction systems, such as the Canadian Forest Fire Danger Rating System, are based on handcrafted features and require installation and maintenance of expensive instruments on the ground, which can be a challenge for developing countries such as Indonesia. We…