CrossCD-Eval: a unified framework with domain-aware feature normalization for cross-domain remote sensing change detection
提出统一框架CrossCD-Eval评估跨域遥感变化检测，并设计DAFN模块提升跨域泛化。
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#深度学习
Temporal forecasting of PM$$_{2.5}$$, PM$$_{10}$$, NO$$_2$$ using deep learning models on Noida Sector 125 and Noida Sector 62
用CNN、RNN、LSTM、GRU、BiLSTM及混合BiLSTM-GRU对诺伊达两站点PM2.5、
Pixel-level high-throughput estimation of crop photosynthetic phenotyping parameters using a multi-task deep learning framework
提出像素级多任务深度学习框架，从高光谱数据高通量估算作物光合表型参数。
From pixels to runoff: integrating high-resolution remote sensing and deep learning for hydrologically meaningful urban land-use classification
比较高分辨率遥感分类精度与水文模拟效果，发现像素精度不足以判断土地利用图的水文适用性。
Deep learning-based monitoring and spatiotemporal analysis of wildlife and human activities using camera trap imagery
用YOLO模型从红外相机图像中检测野生动物与人类，并分析其时空共现格局。
Debris Flow Susceptibility Mapping Using Explainable Deep Learning in Eastern Hindukush Pakistan
用CNN结合SHAP绘制巴基斯坦东兴都库什泥石流易发性图并解释关键因子。
Fine-Tuned Hybrid Deep Learning Framework for Plant Disease Classification Using MobileNetV2 and EfficientNetB0 Feature Fusion
融合MobileNetV2与EfficientNetB0特征并微调，实现植物病害高精度分类。
IIARD INTERNATIONAL JOURNAL OF GEOGRAPHY AND ENVIRONMENTAL MANAGEMENT·9月25日 · 周五63 分 论文遥感与农情监测智慧农业 / 农业物联网阅读 8
Multi-Target Remote Sensing Segmentation with Cross-Scale Attention for Urban-Ecological Intelligence
评估UNet、SegNet和TransU-Net在多目标遥感语义分割中的性能，用于城市与生态监测。
Long-term dynamic root monitoring based on classification fusion and attention mechanism
Long-term dynamic root monitoring based on classification fusion and attention mechanism。Biosystems Engineering
Enhanced YOLOv8-seg-SPDConv for accurate Schatzker classification of tibial plateau fractures
提出轻量YOLOv8n-seg-SPDConv模型，用CT图像实现胫骨平台骨折Schatzker分型
Artificial Intelligence-Enabled Mangrove Ecosystem Monitoring Using Remote Sensing and Environmental Data
融合遥感影像与环境数据，用深度学习构建红树林生态系统健康监测框架。
Cotton leaf disease classification using deep learning models for smart agriculture
Cotton leaf disease classification using deep learning models for smart agriculture。Agriculture & Food Security
Downscaling of SMAP Soil Moisture Based on the Transformer Algorithm in Anhui Province
基于Transformer及变体融合多源卫星与地下水数据，将SMAP土壤湿度降尺度至1km日尺度。
Leakage-aware, calibrated, and explainable deep learning for robust almond disease classification
提出防泄漏、校准且可解释的深度学习框架，用于稳健的杏仁病害分类。
Identification of granitic pegmatites based on GF-5 hyperspectral data and LSTM-Transformer model
提出数据增强驱动的LSTM-Transformer框架，用GF-5高光谱数据识别花岗伟晶岩。
Explainable growth stage classification of cacao (Theobroma cacao L.) leaves and key feature visualization using vision transformer and transfer learning
用ViT和迁移学习对可可叶D、E期进行可解释分类，准确率达97%。
Forecasting Urbanization Dynamics On İstanbul’s European Side Using Deep Learning And Extrapolation Techniques
用Landsat 8影像和U-Net深度学习模型分析伊斯坦布尔欧洲侧2013-2024年城市化动态并
A comprehensive review of deep learning methods for weed classification in precision agriculture
综述2018-2025年深度学习杂草分类方法，分析技术瓶颈并给出改进建议。
Deep learning-driven multisource remote sensing image fusion: Advances, challenges, and future directions
综述深度学习多源遥感图像融合方法、挑战与未来方向。
A Hybrid CNN–Transformer–LSTM Deep Learning Framework for Automated Cotton Disease Detection
提出CNN-Transformer-LSTM混合框架，实现棉花叶片病害自动识别。