Remote sensing benefits habitat conservation by making monitoring of large areas easier compared to field surveying especially if the remote sensed data can be automatically analyzed. An important aspect of monitoring is classifying and mapping habitat types present in the monitored area. Automatic classification is a difficult task, as classes have fine-grained differences and their distributions are long-tailed and unbalanced. Usually training data used for automatic land cover classification relies on fully annotated segmentation maps, annotated from remote sensed imagery to a fairly high-level taxonomy, i.e., classes such as forest, farmland, or urban area. A challenge with automatic habitat classification is that reliable data annotation requires field-surveys. Therefore, full segmentation maps are expensive to produce, and training data is often sparse, point-like, and limited to areas accessible by foot. Methods for utilizing these limited data more efficiently are needed.
We address these problems by proposing a method for habitat classification and mapping, and apply this method to classify the entire northern Finnish Lapland area into Natura2000 classes. The method is characterized by using finely-grained, sparse, single-pixel annotations collected from the field, combined with large amounts of unannotated data to produce segmentation maps. Supervised, unsupervised and semi-supervised methods are compared, and the benefits of transfer learning from a larger out-of-domain dataset are demonstrated. We propose a \ac{CNN} biased towards center pixel classification ensembled with a random forest classifier, that produces higher quality classifications than the models themselves alone. We show that cropping augmentations, test-time augmentation and semi-supervised learning can help classification even further.
Automated classification of natural habitats using ground-level imagery
Centre for Environmental Intelligence, University of Exeter（埃克塞特大学环境智能中心）；Department of Computer Science, Faculty of Environment, Science and Economy, University of Exeter（埃克塞特大学环境、科学与经济学院计算机科学系）；Natural England（自然英格兰）
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）
Beluga Whale Detection from Satellite Imagery with Point Labels
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院 aerospace information research institute）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子、电气与通信工程学院）；College of Resources and Environment, University of Chinese Academy of Sciences（中国科学院大学资源与环境学院）；Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences（中国科学院地理科学与自然资源研究所）；Chinese Research Academy of Environmental Sciences（中国环境科学研究院）；School of Future Technology, University of Chinese Academy of Sciences（中国科学院大学未来技术学院）；Technical Institute of Physics and Chemistry, Chinese Academy of Sciences（中国科学院物理化学技术研究所）
本文提出结合点标注与 SAM 自动生成边界框并训练 YOLOv8 的流程，实现 VHR 卫星影像中确定鲸鱼、不确定鲸鱼和竖琴海豹的多类别检测。
Counting Trees from Satellite Imagery with Noisy Supervision
University of Copenhagen（哥本哈根大学）；University of Rwanda（卢旺达大学）；University of Chinese Academy of Sciences（中国科学院大学）；University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；Stony Brook University（石溪大学）；LIGM, CNRS, Univ Gustave Eiffel, ENPC, IPP（LIGM，CNRS，古斯塔夫·埃菲尔大学，ENPC，IPP）
Predicting butterfly species presence from satellite imagery using soft contrastive regularisation
The Alan Turing Institute, the UK（英国阿尔安·图灵研究所）；Wageningen University & Research, the NL（瓦赫宁根大学与研究中心）；University College London, the UK（伦敦大学学院）；UK Centre for Ecology & Hydrology, the UK（英国生态与水文研究中心）
Mapping illegal waste dumping sites with neural-network classification of satellite imagery
Dymaxion Labs；Fundación Bunge y Born（邦赫与邦基金会）
公共卫生和生境质量是城市规划的重要目标。近年来，在可供决策的信息稀缺的背景下，非法废物倾倒场的严重社会和环境影响使其成为全球南方城市面临的最严重问题之一。为了帮助确定倾倒地点的位置并跟踪其随时间的演变，我们采用了机器学习领域的数据驱动模型，分析卫星图像。这使我们能够利用地理空间开放数据、高分辨率卫星图像和开源工具的日益可用性，利用布宜诺斯艾利斯的一小部分已知垃圾倾倒场来训练机器学习算法，然后以高速度和低成本预测其他场地在广阔地区的位置。本案例研究展示了Dymaxion Labs和Fundación Bunge y Born合作的成果，利用这一技术创建了该地区非法废物倾倒场潜在位置的综合地图。
Wetland mapping from sparse annotations with satellite image time series and temporal-aware segment anything model
Department of Geography, The University of Hong Kong, Hong Kong, China（香港大学地理系）；Pengcheng Laboratory, Shenzhen, China（深圳鹏城实验室）；Department of Electronics and Information Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China（哈尔滨工业大学（深圳）电子与信息工程学院）；Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Ministry of Natural Resources, Guangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, China（粤港澳大湾区地理环境监测重点实验室，自然资源部，广东省城市信息学重点实验室，深圳空间智能感知与服务重点实验室，深圳大学）
AquaCluster: Using Satellite Images And Self-supervised Machine Learning Networks To Detect Water Hidden Under Vegetation
Division of Water and Environmental Engineering, KTH Royal Institute of Technology（水与环境工程系，皇家理工学院）；Division of Software and Computer Systems, KTH Royal Institute of Technology（软件与计算机系统系，皇家理工学院）；Department of Physical Geography, Stockholm University（物理地理系，斯德哥尔摩大学）
SAM-Aug: Leveraging SAM Priors for Few-Shot Parcel Segmentation in Satellite Time Series
Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Jiangnan University（轻工业先进过程控制重点实验室，教育部，江南大学）；Academy of Biology and Biotechnology, Southern Federal University（生物与生物技术学院，南方联邦大学）；State Key Lab of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（测绘遥感信息工程国家重点实验室，武汉大学）
High-resolution optical and acoustic remote sensing datasets of the Puck Lagoon, Southern Baltic
Gdynia Maritime University（格丁尼亚海事大学）；Maritime Institute（海事研究所）；Cyprus University of Technology（塞浦路斯理工大学）；Technische Universität Berlin（柏林工业大学）；Gdańsk University of Technology（格但斯克理工大学）；Nicolaus Copernicus University in Toruń（托伦尼古拉·哥白尼大学）；Centre for Underwater Archaeology（水下考古中心）；University of Adelaide（阿德莱德大学）；Technical University Wien（维也纳工业大学）；MEWO SA（MEWO股份公司）；National Technical University of Athens（雅典国立技术大学）
AnchorScore: A CLIP-Based Diagnostic of MLLM Annotation Difficulty
School of Foreign Studies, Changsha University of Science and Technology（长沙理工大学外国语学院）；School of Education, Hunan Agricultural University（湖南农业大学教育学院）；School of Information and Intelligence, Hunan Agricultural University（湖南农业大学信息与智能科学学院）
