Deep Learning for Slum Mapping in Remote Sensing Images: A Meta-analysis and Review
Centre of Excellence in Artificial Intelligence, Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校人工智能卓越中心）；Tata Consultancy Services Research（塔塔咨询服务公司研究院）
The major Sustainable Development Goals (SDG) 2030, set by the United Nations Development Program (UNDP), include sustainable cities and communities, no poverty, and reduced inequalities. However, millions of people live in slums or informal settlements with poor living conditions in many major cities around the world, especially in less developed countries. To emancipate these settlements and their inhabitants through government intervention, accurate data about slum location and extent is required. While ground survey data is the most reliable, such surveys are costly and time-consuming. An alternative is remotely sensed data obtained from very high-resolution (VHR) imagery. With the advancement of new technology, remote sensing based mapping of slums has emerged as a prominent research area. The parallel rise of Artificial Intelligence, especially Deep Learning has added a new dimension to this field as it allows automated analysis of satellite imagery to identify complex spatial patterns associated with slums. This article offers a detailed review and meta-analysis of research on slum mapping using remote sensing imagery from 2014 to 2024, with a special focus on deep learning approaches. Our analysis reveals a trend towards increasingly complex neural network architectures, with advancements in data preprocessing and model training techniques significantly enhancing slum identification accuracy. We have attempted to identify key methodologies that are effective across diverse geographic contexts. While acknowledging the transformative impact Convolutional Neural Networks (CNNs) in slum detection, our review underscores the absence of a universally optimal model, suggesting the need for context-specific adaptations. We also identify prevailing challenges in this field, such as data limitations and a lack of model explainability and suggest potential strategies for overcoming these.
SLUM-i: Semi-supervised Learning for Urban Mapping of Informal Settlements and Data Quality Benchmarking
School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST)（电气工程与计算机科学学院，国立科学与技术大学（NUST））；Smart Data & Knowledge Services, German Research Center for Artificial Intelligence (DFKI)（智能数据与知识服务，德国人工智能研究中心（DFKI））
Semantic-Guided Cross-Sensor Super Resolution of Remote Sensing Images: A Gated Dual Conditioning Flow Matching Model
School of Computing and Augmented Intelligence（计算与增强智能学院）；School of Geographical Sciences and Urban Planning（地理科学与城市规划学院）；Arizona State University（亚利桑那州立大学）
Training a modern deep neural network on massive labeled samples is the main paradigm in solving the scene classification problem for remote sensing, but learning from only a few data points remains a challenge. Existing methods for few-shot remote sensing scene classification are performed in a sample-level manner, resulting in easy overfitting of learned features to individual samples and inadequate generalization of learned category segmentation surfaces. To solve this problem, learning should be organized at the task level rather than the sample level. Learning on tasks sampled from a task…
Deep learning (DL) algorithms are considered as a methodology of choice for remote-sensing image analysis over the past few years. Due to its effective applications, deep learning has also been introduced for automatic change detection and achieved great success. The present study attempts to provide a comprehensive review and a meta-analysis of the recent progress in this subfield. Specifically, we first introduce the fundamentals of deep learning methods which arefrequently adopted for change detection. Secondly, we present the details of the meta-analysis conducted to examine the status of …
Department of Aerospace Intelligent Science and Technology, School of Astronautics, Beihang University（航天智能科学与技术系，航天学院，北京航空航天大学）；State Key Laboratory of Virtual Reality Technology and Systems, Beihang University（虚拟现实技术与系统国家重点实验室，北京航空航天大学）；Shenyuan Honors College, Beihang University（Shen Yuan荣誉学院，北京航空航天大学）
College of Electronics and Information Engineering, Sichuan University（四川大学电子信息工程学院）；School of Mechanical Engineering and Automation, Fuzhou University（福州大学机械工程与自动化学院）；Yunnan Key Laboratory of Software Engineering, Yunnan University（云南大学云南省软件工程重点实验室）
Remote heart rate estimation is the measurement of heart rate without any physical contact with the subject and is accomplished using remote photoplethysmography (rPPG) in this work. rPPG signals are usually collected using a video camera with a limitation of being sensitive to multiple contributing factors, e.g. variation in skin tone, lighting condition and facial structure. End-to-end supervised learning approach performs well when training data is abundant, covering a distribution that doesn't deviate too much from the distribution of testing data or during deployment. To cope with the unf…
Adaptive Multi-Scale Correlation Meta-Network for Few-Shot Remote Sensing Image Classification
School of Computer Science, UPES, Dehradun, 248007, Uttarakhand, India.（UPES计算机科学学院）；School of Computer Science and Engineering, Galgotias University, India.（加尔各答大学计算机科学与工程学院）；Computer Science and Information Systems, BITS Pilani, Pilani Rajasthan, India.（比什努学院计算机科学与信息系统）
Beyond Templates: Revisiting Zero-Shot Remote Sensing through Meta-Prompting
Remote Sensing Lab, National Technical University of Athens（遥感实验室，国家技术大学雅典分校）；Remote Sensing Lab, Department of Engineering and Sciences, National Technical University of Athens（遥感实验室，工程与科学系，国家技术大学雅典分校）；Universitas Mercatorum（默卡托大学）
In this paper, we deal with the problem of object detection on remote sensing images. Previous methods have developed numerous deep CNN-based methods for object detection on remote sensing images and the report remarkable achievements in detection performance and efficiency. However, current CNN-based methods mostly require a large number of annotated samples to train deep neural networks and tend to have limited generalization abilities for unseen object categories. In this paper, we introduce a few-shot learning-based method for object detection on remote sensing images where only a few anno…
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））
Few-Shot Remote Sensing Scene Classification (FSRSSC) is an important task, which aims to recognize novel scene classes with few examples. Recently, several studies attempt to address the FSRSSC problem by following few-shot natural image classification methods. These existing methods have made promising progress and achieved superior performance. However, they all overlook two unique characteristics of remote sensing images: (i) object co-occurrence that multiple objects tend to appear together in a scene image and (ii) object spatial correlation that these co-occurrence objects are distribut…
Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction
Jiangsu Provincial Key Laboratory for Advanced Remote Sensing and Geographic Information Technology（江苏省先进遥感与地理信息科技重点实验室）；Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources（自然资源部土地卫星遥感应用重点实验室）；School of Geography and Ocean Science, Nanjing University（南京大学地理与海洋科学学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）
We propose an automated method to estimate a road segment's free-flow speed from overhead imagery and road metadata. The free-flow speed of a road segment is the average observed vehicle speed in ideal conditions, without congestion or adverse weather. Standard practice for estimating free-flow speeds depends on several road attributes, including grade, curve, and width of the right of way. Unfortunately, many of these fine-grained labels are not always readily available and are costly to manually annotate. To compensate, our model uses a small, easy to obtain subset of road features along wit…
The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot
University of Western São Paulo (UNOESTE)（圣保罗西部大学）；University of Tennessee（田纳西大学）；Federal University of Mato Grosso do Sul (UFMS)（南马托格罗索联邦大学）；São Paulo State University (UNESP)（圣保罗州立大学）；University of Waterloo（滑铁卢大学）
本文探索了 Meta AI 的 Segment Anything Model (SAM) 在遥感图像分割中的应用，通过结合文本提示与单样本训练的自动化技术提升了多尺度数据上的分割精度，并开源代码以推动 SAM 在遥感领域的进一步适配。
The development of accurate and scalable cross-modal image-text retrieval methods, where queries from one modality (e.g., text) can be matched to archive entries from another (e.g., remote sensing image) has attracted great attention in remote sensing (RS). Most of the existing methods assume that a reliable multi-modal training set with accurately matched text-image pairs is existing. However, this assumption may not always hold since the multi-modal training sets may include noisy pairs (i.e., textual descriptions/captions associated to training images can be noisy), distorting the learning …
University of Cape Town（开普敦大学）；University of Lagos（拉各斯大学）
The remote sensing community has identified data fusion as one of the key challenging topics of the 21st century. The subject of image fusion in two-dimensional (2D) space has been covered in several published reviews. However, the special case of 2.5D/3D Digital Elevation Model (DEM) fusion has not been addressed till date. DEM fusion is a key application of data fusion in remote sensing. It takes advantage of the complementary characteristics of multi-source DEMs to deliver a more complete, accurate and reliable elevation dataset. Although several methods for fusing DEMs have been developed,…
Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学）；University of São Paulo（圣保罗大学）；Federal University of Mato Grosso do Sul（南马托格罗索联邦大学）
Semantic segmentation is a classic computer vision task with multiple applications, which includes medical and remote sensing image analysis. Despite recent advances with deep-based approaches, labeling samples (pixels) for training models is laborious and, in some cases, unfeasible. In this paper, we present two novel meta learning methods, named WeaSeL and ProtoSeg, for the few-shot semantic segmentation task with sparse annotations. We conducted extensive evaluation of the proposed methods in different applications (12 datasets) in medical imaging and agricultural remote sensing, which are …
SpecAware: A Spectral-Content Aware Foundation Model for Unifying Multi-Sensor Learning in Hyperspectral Remote Sensing Mapping
Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities (Ministry of Natural Resources), East China Normal University（空间-时间大数据分析与应用国家级重点实验室（自然资源部），东华师范大学）；School of Geospatial Artificial Intelligence, East China Normal University（地理信息人工智能学院，东华师范大学）；Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University（地理信息科学重点实验室（教育部），东华师范大学）；School of Geographic Sciences, East China Normal University（地理科学学院，东华师范大学）；Shanghai Municipal Institute of Surveying and Mapping（上海市测绘院）；Institute of Defense Engineering, AMS（工程院，AMS）
GeoHeight-Bench: Towards Height-Aware Multimodal Reasoning in Remote Sensing
KTH Royal Institute of Technology（瑞典皇家理工学院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Technical University of Munich（慕尼黑工业大学）
Not Just Learning from Others but Relying on Yourself: A New Perspective on Few-Shot Segmentation in Remote Sensing
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；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）；University of Chinese Academy of Sciences（中国科学院大学）
Design Choices That Matter: A Functional ANOVA Analysis for Remote Sensing Multi-Label Classification
Jožef Stefan Institute（约热夫·斯泰凡研究所）；Jožef Stefan International Postgraduate School（约热夫·斯泰凡国际研究生学院）；Trinity University（三一大学）；Singidunum University（辛吉杜努姆大学）
GeoMeld: Toward Semantically Grounded Foundation Models for Remote Sensing
Indian Institute of Technology Bombay（印度理工学院孟买分校）；Space Applications Centre, ISRO（印度空间研究组织空间应用中心）；University of Trento（特伦托大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Microsoft（微软）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）
Remotely sensed geospatial data are critical for applications including precision agriculture, urban planning, disaster monitoring and response, and climate change research, among others. Deep learning methods are particularly promising for modeling many remote sensing tasks given the success of deep neural networks in similar computer vision tasks and the sheer volume of remotely sensed imagery available. However, the variance in data collection methods and handling of geospatial metadata make the application of deep learning methodology to remotely sensed data nontrivial. For example, satell…
Geospatial-Temporal Sensemaking of Remote Sensing Activity Detections with Multimodal Large Language Model
SenSIP Center, School of ECEE, Arizona State University（SenSIP中心，电子与计算机工程学院，亚利桑那州立大学）；Prime Solutions Group Inc（Prime Solutions Group公司）；Intelligence Advanced Research Projects Activity（智能高级研究计划局）
Trust Before Autonomy: Rethinking Agentic Remote Sensing for Crisis Response
Harokopio University of Athens（雅典哈罗科皮奥大学）；Netcompany-Intrasoft S.A.（Netcompany-Intrasoft公司）；Dimocritus University of Thrace（色雷斯德谟克利特大学）；University of Western Macedonia（西马其顿大学）
A comprehensive review of remote sensing in wetland classification and mapping
Department of Geography, The University of Hong Kong（地理系，香港大学）；Department of Earth System Science, Tsinghua University（地球系统科学系，清华大学）；Department of Electronics and Information Engineering, Harbin Institute of Technology (Shenzhen)（电子与信息工程系，哈尔滨工业大学（深圳））；Pengcheng Laboratory（鹏城实验室）
A Resource-Efficient Training Framework for Remote Sensing Text--Image Retrieval
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；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）
Technische Universität Dortmund（多特蒙德工业大学）；Bergische Universität Wuppertal（伍珀塔尔大学）
We study the quantification of uncertainty of Convolutional Neural Networks (CNNs) based on gradient metrics. Unlike the classical softmax entropy, such metrics gather information from all layers of the CNN. We show for the EMNIST digits data set that for several such metrics we achieve the same meta classification accuracy -- i.e. the task of classifying predictions as correct or incorrect without knowing the actual label -- as for entropy thresholding. We apply meta classification to unknown concepts (out-of-distribution samples) -- EMNIST/Omniglot letters, CIFAR10 and noise -- and demonstra…
