Accurate, consistent, and long-term monitoring of global lake dynamics is essential for understanding the impacts of climate change and human activities on water resources and ecosystems. However, existing methods often require extensive manually collected training data and expert knowledge to delineate accurate water extents of various lake types under different environmental conditions, limiting

3D street scene semantic segmentation is essential for urban understanding. However, supervised point cloud semantic segmentation networks heavily rely on expensive manual annotations and demonstrate limited generalization capabilities across datasets, which poses limitations in a range of downstream tasks. In contrast, image segmentation networks exhibit stronger generalization. Fortunately, mobile

Building damage detection based on optical and SAR remote sensing imagery can mitigate the adverse effects of weather, climate, and nighttime imaging. However, under emergency conditions, inherent limitations such as satellite availability, sensor swath width, and data sensitivity make it challenging to unify the resolution of optical and SAR imagery covering the same area. Additionally, optical imagery

Principles of dense, mixed-use environments and pedestrianisation are influential in urban planning practice worldwide. A key outcome espoused by these principles is generating “urban vitality”, the continuous use of street sidewalk infrastructure throughout the day, to promote safety, economic viability and attractiveness of city neighbourhoods. Vitality is hypothesised to arise from a nearby mixture

Aerial images present significant challenges to label-driven supervised learning, in particular, the annotation of substantial small-sized objects is a highly laborious process. To maximize the utility of scarce labeled data alongside the abundance of unlabeled data, we present a semi-supervised learning pipeline tailored for label-efficient object detection in aerial images. In our investigation,

Time series reconstruction is pivotal for enabling continuous, long-term monitoring of environmental changes, particularly in rapidly evolving coastal ecosystems. Despite the array of developed reconstruction methods, they often fail to be effectively applied in coastal zones. In coastal zones, the dynamic environment and frequent cloud cover undermine the effectiveness of existing methods, making

Place recognition is a fundamental technology for uncrewed systems such as robots and autonomous vehicles, enabling tasks like global localization and simultaneous localization and mapping (SLAM). Existing Place recognition technologies based on vision or LiDAR have made significant progress, but these sensors may degrade or fail in adverse conditions. 4D millimeter-wave radar offers strong resistance

The despeckling task for synthetic aperture radar (SAR) has long faced the challenge of obtaining clean images. Although unsupervised deep learning despeckling methods alleviate this issue, they often struggle to balance despeckling effectiveness and the preservation of spatial details. Furthermore, some unsupervised despeckling approaches overlook the effect of land cover changes when dual-temporal

Precise matching of visual features between frames is crucial for the robustness and accuracy of visual odometry and SLAM (Simultaneous Localization and Mapping) systems. However, factors such as complex illumination and texture variations may cause significant errors in feature correspondences that will degrade the accuracy of visual localization. In this paper, we utilize the feature descriptor to

Rotating Payload Satellite (RPS) utilizes payload rotation to drive the optical axis for vertical orbit scanning, which enables high-resolution and wide-coverage imaging of ground curved targets. However, the presence of irregular image motion degradation (IMD) in the dynamic imaging drastically degrades the imaging quality. High stability and high precision IMD compensation have become key point for

LiDAR-Inertial simultaneous localization and mapping (LI-SLAM) plays a crucial role in various applications such as robot localization and low-cost 3D mapping. However, factors including inaccurate motion distortion estimation and pose graph constraints, and frequent LiDAR feature degeneracy present significant challenges for existing LI-SLAM methods. To address these issues, we propose DALI-SLAM,

Thanks to the application of deep learning technology in point cloud processing of the remote sensing field, point cloud classification has become a research hotspot in recent years. Although existing solutions have made unprecedented progress, they ignore the inherent characteristics of point clouds in remote sensing fields that are strictly arranged according to latitude, longitude, and altitude

Rivers transport large amounts of carbon, serving as a critical link between terrestrial, coastal, and atmospheric biogeochemical cycles. However, our observations and understanding of long-term river carbon dynamics in large-scale remain limited. Integrating machine learning with remote sensing offers an effective approach for quantifying organic carbon (OC) from space. Here, we develop the Aquatic-Organic

As a common method in the field of computer vision, spatial attention mechanism has been widely used in semantic segmentation of remote sensing images due to its outstanding long-range dependency modeling capability. However, remote sensing images are usually characterized by complex backgrounds and large intra-class variance that would degrade their analysis performance. While vanilla spatial attention

Large language models (LLMs) have recently been extended to the vision-language realm, obtaining impressive general multi-modal capabilities. However, the exploration of multi-modal large language models (MLLMs) for remote sensing (RS) data is still in its infancy, lacking datasets and with unsatisfactory performance. In this work, we meticulously curate a large-scale RS multi-modal instruction tuning

Current agricultural parcels (AP) extraction faces two main limitations: (1) existing AP delineation methods fail to fully utilize low-level information (e.g., parcel boundary information), leading to unsatisfactory performance under certain circumstances; (2) the lack of large-scale, high-resolution AP benchmark datasets in China hinders comprehensive model evaluation and improvement. To address the

Subway shield tunnels have emerged as the preferred solution for urban transportation due to their convenience and safety. Constructed using prefabricated concrete segments, these tunnels exhibit structural stability. However, the segment joints and bolt holes are prone to groundwater infiltration under prolonged external stress, potentially compromising the lifespan of the shield tunnels. Consequently

Nature disasters play a key role in shaping human-urban infrastructure interactions. Effective and efficient response to natural disasters is essential for building resilience and sustainable urban environment. Two types of information are usually the most necessary and difficult to gather in disaster response. The first information is about the disaster damage perception, which shows how badly people

Polarimetric Synthetic Aperture Radar (PolSAR) imagery is a valuable tool for Earth observation. This imaging technique finds wide application in various fields, including agriculture, forestry, geology, and disaster monitoring. However, due to the inherent presence of speckle noise, filtering is often necessary to improve the interpretability and reliability of PolSAR data. The effectiveness of a

Very High-Resolution (VHR) urban remote sensing images segmentation is widely used in ecological environmental protection, urban dynamic monitoring, fine urban management and other related fields. However, the large-scale variation and discrete distribution of objects in VHR images presents a significant challenge to accurate segmentation. The existing studies have primarily concentrated on the internal

Building 3D geometric maps of man-made spaces is a well-established and active field that is fundamental to numerous computer vision and robotics applications. However, considering the continuously evolving nature of built environments, it is essential to question the capabilities of current mapping efforts in handling temporal changes. In addition to the above, the ability to create spatiotemporal

Change Detection (CD) focuses on identifying specific pixel-level landscape changes in multi-temporal remote sensing images. The process of obtaining pixel-level annotations for CD is generally both time-consuming and labor-intensive. Faced with this annotation challenge, there has been a growing interest in research on Weakly-Supervised Change Detection (WSCD). WSCD aims to detect pixel-level changes

Multisource data can extract diverse urban functional features, facilitating a deeper understanding of the functional structure of road networks. Street view images and taxi trajectories, as forms of urban geographic big data, capture features of the urban physical environment and travel behavior, serving as effective data sources for identifying the functional structure of urban spaces. However, street

RS scene graphs represent RS scenes as graphs with objects as nodes and their spatial relationships as edges, playing a crucial role in understanding and interpreting RS scenes at a higher level. However, existing RS scene graph generation methods, relying on deep learning models, face limitations due to their dependence on extensive relationship labels, restricted generation accuracy, and limited

In transmission lines, regular inspections are crucial for maintaining their safe operation. Automatic and accurate detection of power transmission facility components (power components) in inspection imagery is an effective way to monitor the status of electrical assets within the Right of Ways (RoWs). However, the multitude of small-scale objects (e.g. grading rings, vibration dampers) in inspection

Owing to the difficulty of utilizing hidden spatio-temporal information, spatio-temporal knowledge graph (KG) reasoning tasks in real geographic environments have issues of low accuracy and poor interpretability. This paper proposes a grid neighborhood-based graph convolutional network (GN-GCN) for spatio-temporal KG reasoning. Based on the discretized process of encoding spatio-temporal data through

Investigating the distribution of ground surface hot spring fluids is crucial for the exploitation and utilization of geothermal resources. The detailed information provided by dual-spectrum images captured by unmanned aerial vehicles (UAVs) flew at low altitudes is beneficial to accurately segment ground surface hot spring fluids. However, existing image segmentation methods face significant challenges

Near-surface air temperature (NSAT) data is essential for climate analysis and applied research in areas with sparse ground-based observations. In recent years, machine learning (ML) techniques have been increasingly used to estimate NSAT, delivering promising results. However, in regions with limited observational samples, machine learning-based NSAT estimations may encounter challenges, potentially

While wetlands have been extensively studied using optical and radar satellite imagery, thermal imagery has been used less often due its low spatial – temporal resolutions and challenges for emissivity estimation. Since 2018, spaceborne thermal imagery has gained interest due to the availability of ECOSTRESS data, which are acquired at 70 m spatial resolution and a 3–5 revisit time. This study aimed

Earth observation from satellite sensors offers the possibility to monitor natural ecosystems by deriving spatially explicit and temporally resolved biogeophysical parameters. Optical remote sensing, however, suffers from missing data mainly due to the presence of clouds, sensor malfunctioning, and atmospheric conditions. This study proposes a novel deep learning architecture to address gap filling

Why should we confine land cover classes to rigid and arbitrary definitions? Land cover mapping is a central task in remote sensing image processing, but the rigorous class definitions can sometimes restrict the transferability of annotations between datasets. Open vocabulary recognition, i.e. using natural language to define a specific object or pattern in an image, breaks free from predefined nomenclature

The utilization of deep learning in Tomographic SAR (TomoSAR) three-dimensional (3D) imaging technology addresses the inefficiency inherent in traditional compressed Sensing (CS)-based TomoSAR algorithms. However, current deep learning TomoSAR imaging methods heavily depend on prior knowledge of observation geometries, as the network training requires a predefined observation prior distribution. Additionally

High-altitude mountain glaciers are highly responsive to environmental changes. However, their remote locations limit the applicability of traditional mapping methods, such as probing and Ground Penetrating Radar (GPR), in tracking changes in ice thickness and glacier volume. Over the past two decades, airborne Tomographic Synthetic Aperture Radar (TomoSAR) has shown promise for mapping the internal

Shorelines derived from optical satellite images are increasingly being used for regional to global scale analysis of sandy coastline dynamics. The optical satellite record, however, is contaminated by cloud cover, which can substantially reduce the temporal resolution of available images for shoreline analysis. Meanwhile, with the development of deep learning methods, optical images are increasingly

From 2012 to 2015, California experienced the most severe drought since 1895, causing natural vegetation throughout the state to become water-stressed. With many areas in California being inaccessible and having extremely rugged terrain, remote sensing provides a means for monitoring plant stress across a broad landscape. Airborne hyperspectral and thermal imaging captured the drought in the spring

Synthetic aperture radar (SAR) image positioning technology is extensively used in many scientific fields, including land surveying and mapping. Geometric self-calibration can be performed if images are captured in three directions. However, when the number of images is too small, self-calibration of the SAR images based on the range-Doppler (RD) model appears to be inaccurate. Hence, a robust geometric

Land cover information is indispensable for advancing the United Nations’ sustainable development goals, and land cover mapping under a more detailed category system would significantly contribute to economic livelihood tracking and environmental degradation measurement. However, the substantial difficulty in acquiring fine-grained training data makes the implementation of this task particularly challenging

Synthetic Aperture Radar (SAR) imagery is a powerful and widely used tool in a variety of remote sensing applications. The increasing number of SAR sensors makes it challenging to process and store such a large amount of data. In addition, as the flexibility and processing power of on-board electronics increases, the challenge of effectively transmitting large images to the ground becomes more tangible

The study of estuarine turbidity maximum (ETM) has a long history. However, the algorithms and criteria for ETM identification vary significantly across estuaries and hydrological regimes. Moreover, almost all of these methods depend on derived water parameters, such as suspended sediment concentration and turbidity, which inevitably result in inherent errors in the ETM results. To overcome these disadvantages

Chlorophyll-a (Chla) retrieval from satellite observations is crucial for assessing water quality and the health of aquatic ecosystems. Utilizing satellite data, while invaluable, poses challenges including inherent satellite biases, the necessity for precise atmospheric correction (AC), and the complexity of water bodies, all of which complicate establishing a reliable relationship between remote

Weather and climate forecasts use the distribution of sea surface temperature (SST) as a critical factor in atmosphere–ocean interactions. High spatial resolution SST data are typically produced using infrared sensors, which use channels with wavelengths ranging from approximately 3.7 to 12 µm. However, SST data retrieved from infrared sensor-based satellites often contain noise and missing areas due

The acceleration of global warming and intensifying global climate anomalies have led to a rise in the frequency of wildfires. However, most existing research on wildfire fields focuses primarily on wildfire identification and prediction, with limited attention given to the intelligent interpretation of detailed information, such as fire front within fire region. To address this gap, advance the analysis

In response to the challenges posed by the difficulty in obtaining polarimetric synthetic aperture radar (PolSAR) data for certain specific categories of targets, we present a zero-shot target recognition method for PolSAR images. Based on a generative model, the method leverages the unique characteristics of polarimetric SAR images and incorporates two key modules: the scattering characteristics-guided

We delve into the nascent field of underwater image captioning from three perspectives: challenges, models, and datasets. One challenge arises from the disparities between natural images and underwater images, which hinder the use of the former to train models for the latter. Another challenge exists in the limited feature extraction capabilities of current image captioning models, impeding the generation

Monitoring agricultural areas, given their rapid transformation and small-scale spatial changes, necessitates obtaining dense time series of high-resolution remote sensing data. In this manner, the unmanned aerial vehicle (UAV) that can provide high-resolution images is indispensable for monitoring and assessing agricultural areas, especially for rapidly changing crops like alfalfa. Considering the

Timely and accurate mapping of rice cultivation distribution is crucial for ensuring global food security and achieving SDG2. From a global perspective, rice areas display high heterogeneity in spatial pattern and SAR time-series characteristics, posing substantial challenges to deep learning (DL) models’ performance, efficiency, and transferability. Moreover, due to their “black box” nature, DL often

Timely and accurate monitoring of impervious surface areas (ISA) is crucial for effective urban planning and sustainable development. Recent advances in remote sensing technologies have enabled global ISA mapping at fine spatial resolution (<30 m) over long time spans (>30 years), offering the opportunity to track global ISA dynamics. However, existing 30 m global long-term ISA datasets suffer from

Accurate mapping of rubber plantations in Southeast Asia is critical for sustainable plantation management and ecological and environmental impact assessment. Despite extensive research on rubber plantation mapping, studies have largely been confined to provincial scales, with the few country-scale assessments showing significant disagreement in both spatial distribution and area estimates. These discrepancies

Intermittent Rivers and Ephemeral Streams (IRES) are the major sources of flowing water on Earth. Yet, their dynamics are challenging for optical and radar satellites to monitor due to the heavy cloud cover and narrow water surfaces. The significant backscattering mechanism change and image mismatch further hinder the joint use of optical-SAR images in IRES monitoring. Here, a Deep data fusion-based

The identification of individual trees can reveal the competitive and symbiotic relationships among trees within forest stands, which is fundamental understand biodiversity and forest ecosystems. Highly precise identification of individual trees can significantly improve the efficiency of forest resource inventory, and is valuable for biomass measurement and forest carbon storage assessment. In previous

Superpixel generation is an essential preprocessing step for intelligent interpretation of object-level Polarimetric Synthetic Aperture Radar (PolSAR) images. The Simple Linear Iterative Clustering (SLIC) algorithm has become one of the primary methods for superpixel generation in PolSAR images due to its advantages of minimal human intervention and ease of implementation. However, existing SLIC-based

Surface reconstruction in street scenes is a critical task in computer vision and photogrammetry, with images and LiDAR point clouds being commonly used data sources. However, image-only reconstruction faces challenges such as lighting variations, weak textures, and sparse viewpoints, while LiDAR-only methods suffer from issues like sparse and noisy LiDAR point clouds. Effectively integrating these

Recently, the importance of seagrasses in the functioning of coastal ecosystems and their ability to mitigate climate change has gained increased recognition. However, there has been a rapid global deterioration of seagrass ecosystems due to climate change and human-mediated disturbances. Accurate broad-scale mapping of seagrass extent is necessary for seagrass conservation and management actions.

Non-stand replacing disturbances (NSRs) are events that do not result in complete removal of trees and generally occur at a low intensity over an extended period of time (e.g., insect infestation), or at spatially variable intensities over short time intervals (e.g., windthrow). These disturbances alter the quality and quantity of forest biomass, impacting timber supply and ecosystem services, making

Phase unwrapping (PhU) is one of the most critical steps in synthetic aperture radar interferometry (InSAR) technology. However, the current phase unwrapping methods cannot completely avoid the PhU errors, particularly in complex environments with low coherence. Here, we show that the PhU errors can be corrected well with the time series interferograms. We propose a three-dimensional automatic detection

Spaceborne light detection and ranging (LiDAR) waveform sensors require accurate signal simulations to facilitate prelaunch calibration, postlaunch validation, and the development of land surface data products. However, accurately simulating spaceborne LiDAR waveforms over heterogeneous forests remains challenging because data-driven methods do not account for complicated pulse transport within heterogeneous

Simulated remote sensing images bear great potential for many applications in the field of Earth observation. They can be used as controlled testbed for the development of signal and image processing algorithms or can provide a means to get an impression of the potential of new sensor concepts. With the rise of deep learning, the synthesis of artificial remote sensing images by means of deep neural

Point clouds, which are a fundamental type of 3D data, play an essential role in various applications like 3D reconstruction, autonomous driving, and robotics. However, point clouds generated via measuring the time-of-flight of emitted and backscattered laser pulses of TLS, frequently include false points caused by mirror-like reflective surfaces, resulting in degradation of data quality and fidelity

Reconstructing urban areas in 3D from satellite raster images has been a long-standing problem for both academical and industrial research. While automatic methods achieving this objective at a Level Of Detail (LOD) 1 are mostly efficient today, producing LOD2 models is still a scientific challenge. In particular, the quality and resolution of satellite data is too low to infer accurately the planar