融合遥感与环境数据，用神经网络预测鸟类栖息地迁移。
Modeling Habitat Shifts: Integrating Convolutional Neural Networks and Tabular Data for Species Migration Prediction
- 用CNN提取遥感图像的空间特征，结合表格数据建模
- 在多种气候下预测准确率达85%，表现稳定可靠
- 适合生态监测、气候变化研究者使用
由于气候变化，许多栖息地正发生地理范围迁移（Piguet, 2011）。本文提出一种结合卷积神经网络（CNN）与表格数据的方法，用于准确判断鸟类是否存在于特定栖息地。该方法利用卫星影像和环境特征（如温度、降水、高程）预测鸟类在不同气候区的分布。CNN模型捕捉森林覆盖、水体、城市化等景观空间特征，表格方法则处理生态与地理数据。两者联合预测鸟类分布，平均准确率达85%，提供了一种可扩展且可靠的鸟类迁徙建模方案。
原文摘要 · Abstract (English)
Due to climate-induced changes, many habitats are experiencing range shifts away from their traditional geographic locations (Piguet, 2011). We propose a solution to accurately model whether bird species are present in a specific habitat through the combination of Convolutional Neural Networks (CNNs) (O'Shea, 2015) and tabular data. Our approach makes use of satellite imagery and environmental features (e.g., temperature, precipitation, elevation) to predict bird presence across various climates. The CNN model captures spatial characteristics of landscapes such as forestation, water bodies, and urbanization, whereas the tabular method uses ecological and geographic data. Both systems predict the distribution of birds with an average accuracy of 85%, offering a scalable but reliable method to understand bird migration.
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