Synthetic aperture radar (SAR) imaging is capable of observing objects in nearly all weather and illumination conditions and has become an indispensable means of information acquisition for analysis and recognition of objects and scenes. SAR automatic target recognition (SAR ATR) has been one of the most fundamental and challenging problems in remote sensing image analysis. Nowadays, artificial intelligence (AI) technology, represented by large models and AI agents, has transformed the research paradigm, has profoundly influenced various research fields, and continues to evolve at an unprecedented pace. However, the huge potential of AI for SAR image analysis remains locked. To unlock the potential of AI in SAR image understanding, the research community should rethink how to enable bidirectional empowerment between AI and SAR image understanding and strive to achieve substantial breakthroughs at critical bottlenecks. Given this period of remarkable evolution, this article offers the first comprehensive review of SAR ATR, tracing its development and milestones over the past five decades and providing the research community with a clear roadmap. This survey includes approximately 260 research contributions, covering critical aspects of SAR ATR: pivotal challenges, important datasets, the merits and limitations of representative methods, evaluation metrics, and state-of-the-art performance. Finally, we finish the survey by identifying promising directions for future research. Looking ahead, we call for significant attention on three fundamental pillars: the curation of high-quality large-scale datasets, the design of fair and comprehensive evaluation benchmarks, and the fostering of safe open source ecosystems.
Since the beginning of this century, human activities have increasingly gravitated toward coastal areas with a marked rise in their frequency and intensity. This trend has progressively disrupted the balance between humans and natural systems to the point where anthropogenic pressures in many coastal areas have outweighed those induced by natural processes. In this case, research on the coastal zone has taken on heightened significance and urgency, particularly in relation to the challenge of achieving coastal sustainability. Traditional data collection approaches and analytical techniques are becoming inadequate for addressing the complex scenarios in the coastal zone, failing to meet the demands of an ever-growing array of emerging topics. Remote sensing, as a data-intensive tool, has been widely applied in coastal research for a long time, witnessing its evolution. As the research paradigm shifts toward sustainable development, the continuous integration of these two fields presents promising prospects while also posing emerging challenges. More specifically, can remote sensing meet the escalating data and technical requirements demanded by coastal sustainability research? From a remote sensing viewpoint, this article revisits its historical roles and contributions in coastal studies, with the aim of evaluating its potential applicability to the pursuit of coastal sustainability. Facing the impending challenges, we further detail recent advancements in remote sensing data and techniques, giving in-depth consideration to both the resolved and emerging problems from coastal sustainability studies, ultimately charting strategic pathways for future opportunities. By bringing together all achievements in the adoption of remote sensing across coastal regions and examining its role in Earth system science, we present a clear and systematic overview of coastal remote sensing and its applicability to the issue of coastal sustainability.
Estimating the construction year of buildings is critical for advancing sustainability, as older structures often lack energy-efficient features. Sustainable urban planning relies on accurate building age data to reduce energy consumption and mitigate climate change. In this work, we introduce MapYourCity, a novel multimodal benchmark dataset comprising top-view very high-resolution (VHR) imagery, multispectral Earth observation (EO) data from the Copernicus Sentinel-2 constellation, and co-localized street-view images across various European cities. Each building is labeled with its construction epoch, and the task is formulated as a seven-class classification problem covering periods from 1900 to the present. To advance research in EO generalization and multimodal learning, we organized a community-driven data challenge in 2024, hosted by the European Space Agency (ESA) Φ-lab, which ran for four months and attracted wide participation. This article presents the top four performing models from the challenge and their evaluation results. We assess model generalization on cities excluded from training to prevent data leakage, and evaluate performance under missing modality scenarios, particularly when street-view data are unavailable. Results demonstrate that building age estimation is both feasible and effective, even in previously unseen cities and when relying solely on top-view satellite imagery (i.e. with VHR and Sentinel-2 images). The MapYourCity dataset thus provides a valuable resource for developing scalable, real-world solutions in sustainable urban analytics.
Explainable artificial intelligence (XAI) has become increasingly central to remote sensing (RS), as learning-based models are embedded in scientific analysis, operational monitoring, and high-stakes decision making. However, despite the growing number of methods and approaches, explainability is often treated as a transferable capability and assessed using generic criteria inherited from computer vision or tabular learning. Such practices neglect the fact that RS data are governed by strong spatial dependence, spectral redundancy, temporal dynamics, and physical sensing constraints, which fundamentally condition the scientific and operational validity of the explanations. This article revisits XAI in RS from a domain-constrained perspective, framing explainability as a conditional construct whose legitimacy depends on its alignment with the spatial, spectral, temporal, and physical structures of Earth observation data. Rather than cataloging techniques, it synthesizes explainability paradigms, their underlying computational assumptions, evaluation practices, and application regimes to reveal recurrent failure modes that arise when generic XAI approaches are applied without regard to RS-specific constraints. By demonstrating why explanations that appear faithful under standard metrics may be scientifically invalid or operationally misleading, this article reframes explainability around explanatory validity rather than method availability. The resulting framework provides a principled basis for reasoning about XAI choices in RS systems and complements existing works by explicitly linking data structures, model behavior, and interpretability requirements.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
