We introduce VistaFormer, a lightweight Transformer-based model architecture for the semantic segmentation of remote-sensing images. This model uses a multi-scale Transformer-based encoder with a lightweight decoder that aggregates global and local attention captured in the encoder blocks. VistaFormer uses position-free self-attention layers which simplifies the model architecture and removes the need to interpolate temporal and spatial codes, which can reduce model performance when training and testing image resolutions differ. We investigate simple techniques for filtering noisy input signals like clouds and demonstrate that improved model scalability can be achieved by substituting Multi-Head Self-Attention (MHSA) with Neighbourhood Attention (NA). Experiments on the PASTIS and MTLCC crop-type segmentation benchmarks show that VistaFormer achieves better performance than comparable models and requires only 8% of the floating point operations using MHSA and 11% using NA while also using fewer trainable parameters. VistaFormer with MHSA improves on state-of-the-art mIoU scores by 0.1% on the PASTIS benchmark and 3% on the MTLCC benchmark while VistaFormer with NA improves on the MTLCC benchmark by 3.7%.
Multimodal Urban Tree Detection from Satellite and Street-Level Imagery via Annotation-Efficient Deep Learning Strategies
Department of Computer Science, University of California, Davis（加州大学戴维斯分校计算机科学系）；Department of Biological and Agricultural Engineering, University of California, Davis（加州大学戴维斯分校生物与农业工程系）
Scalable Dynamic Origin-Destination Demand Estimation Enhanced by High-Resolution Satellite Imagery Data
Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA（土木与环境工程系，卡内基梅隆大学）；Fujitsu Research of America, Pittsburgh, PA（富士通美国研究院）；Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA（Heinz信息学院）
SeasonStereo: Robust Dense Stereo Matching for Multi-Date Satellite Imagery via Generative AI
Eurecat（欧罗卡特技术中心）；IIE, Facultad de Ingeniería, Universidad de la República（乌拉圭共和国大学工程学院IIE）；Universitat Pompeu Fabra（庞培法布拉大学）；Université Paris-Saclay（巴黎萨克雷大学）；CNRS（法国国家科学研究中心）；ENS Paris-Saclay（巴黎萨克雷高等师范学校）；Institut Universitaire de France（法国大学研究院）
2-speed network ensemble for efficient classification of incremental land-use/land-cover satellite image chips
University of Technology Sydney（悉尼科技大学）；IBM Australia Limited（IBM澳大利亚有限公司）；University of New England（新英格兰大学）；King Abdulaziz University（阿卜杜勒阿齐兹国王大学）；Universiti Kebangsaan Malaysia（马来西亚国民大学）
不断增长的卫星图像数据量对行业和政府提出了挑战，这些行业和政府在及时分析超大数据集的基础上做出数据驱动的决策。用于卫星图像自动分类的常用深度学习算法是时间和资源密集型的训练。当新的图像数据和/或类被添加到训练语料库时，大数据背景下的再训练成本提出了实际挑战。认识到需要一个适应性强，准确，可扩展的卫星图像芯片分类方案，在这项研究中，我们提出了一个合奏：i）缓慢的训练，但高精度的视觉Transformer ;和ii）快速训练，低参数卷积神经网络。视觉Transformer模型提供了可扩展且准确的基础模型。高速CNN提供了一种将新标记的数据纳入分析的有效方法，但以较低的准确性为代价。为了模拟增量数据，将非常大的（约400，000张图像）So2 Sat LCZ 42卫星图像芯片数据集分为四个间隔，每个间隔重新训练高速CNN，每半个间隔训练视觉Transformer。这种实验设置模拟了数据量和多样性随时间的增加。对于自动土地覆盖/土地利用分类的任务，每个数据增量的集成模型优于每个组件模型，最好的准确度为65%，对一个保持测试分区的So2 Sat数据集。建议的合奏和交错的训练时间表提供了一个可扩展的和具有成本效益的卫星图像分类计划，优化处理非常大量的卫星数据。
Leveraging ChatGPT's Multimodal Vision Capabilities to Rank Satellite Images by Poverty Level: Advancing Tools for Social Science Research
Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Sweden（应用智能系统研究中心（CAISR），哈马斯特德大学，瑞典）；Department of Human Geography, Lund University, Sweden（人类地理系，吕勒奥大学，瑞典）；Department of Earth and Environmental Sciences, Lund University, Sweden（地球与环境科学系，吕勒奥大学，瑞典）
FLNet: Flood-Induced Agriculture Damage Assessment using Super Resolution of Satellite Images
Annam.AI CoE, MoE, Indian Institute of Technology Ropar, Punjab, India（Annam.AI联合研究所、教育部、印度理工学院罗帕尔分校）；Department of Mathematical Sciences, Rajiv Gandhi Institute of Petroleum Technology, Jais, UP, India（数学科学系、拉贾吉安石油技术学院）；Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Punjab, India（计算机科学与工程系、印度理工学院罗帕尔分校）
Enhancing Ship Classification in Optical Satellite Imagery: Integrating Convolutional Block Attention Module with ResNet for Improved Performance
Sungkyunkwan University School of Medicine（成均馆大学医学院）；The Coala Inc.（考拉公司）；Korea Science Academy of Korea Advanced Institute of Science and Technology（韩国科学技术院韩国科学学院）；Seoul National University School of Earth and Environmental Sciences（首尔国立大学地球与环境科学学院）
