Satellite measurements of the column-averaged dry air mole fraction of carbon dioxide (XCO2) have been successfully employed to quantify anthropogenic carbon emissions under clean atmospheric conditions. However, for some large anthropogenic sources such as megacities or coal-fired power plants, which are often accompanied by high aerosol loads, especially in developing countries, atmospheric XCO2

Time series from optical sensors are frequently used to retrieve phenology information of forests. While SAR (synthetic aperture radar) sensors can potentially provide even denser time series than optical data, their potential to retrieve phenological information of forests is still underexplored. In addition, the backscatter information from SAR is frequently exploited in the same way (e.g., via dynamic

Satellite-derived actual evapotranspiration (ETa) maps are essential for the development of innovative water management strategies. Over the past decades, multiple novel satellite remote sensing-based surface energy balance (SEB) ETa modeling tools have been widely used to account for field-scale crop water use and irrigation monitoring. However, their predictive capabilities for intensively irrigated

Tomographic synthetic aperture radar (TomoSAR) technology, as an extension of interferometric SAR (InSAR), solves the layover problem and realizes three-dimensional (3-D) imaging. Now, it is an important research direction in the field of radar imaging. However, TomoSAR usually requires the SAR sensor to make enough acquisitions at different spatial locations to achieve high-quality 3-D imaging, which

Street view images (SVIs) coupled with computer vision (CV) techniques have become powerful tools in the planning and related fields for measuring the built environment. However, this methodology is often challenging to be implemented due to challenges in capturing a comprehensive set of planning-relevant environmental attributes and ensuring adequate accuracy. The shortcomings arise primarily from

Cyanobacteria blooms are a threat to water quality of lakes and reservoirs worldwide, requiring scalable monitoring solutions. Existing approaches for remote sensing of cyanobacteria focus on quantifying (accessory) photosynthetic pigment to map surface accumulations. These approaches have proven challenging to validate against in situ observations, limiting uptake in water quality management. Optical

The Synthetic Aperture Radar (SAR) altimeter is an active sensor, which is widely used in satellite microwave remote sensing. It can be also used for geophysical localization by evaluating the similarity between the acquired terrain profile and the prior data. However, typical factors, such as the linear assumption of terrain, high variation of the ground elevation, and wide beam width will degrade

Light Detection and Ranging (LiDAR) technology has demonstrated significant effectiveness in forest remote sensing. Terrestrial Laser Scanning (TLS) and Drone Laser Scanning (DLS) systems reconstruct forest point clouds from distinct perspectives. However, a single-platform point cloud is insufficient for a comprehensive reconstruction of multi-layered forest structures. Therefore, registration of

Multi-View Photometric Stereo (MVPS) is a popular method for fine-detailed 3D acquisition of an object from images. Despite its outstanding results on diverse material objects, a typical MVPS experimental setup requires a well-calibrated light source and a monocular camera installed on an immovable base. This restricts the use of MVPS on a movable platform, limiting us from taking MVPS benefits in

Reducing the reliance of remote sensing semantic segmentation models on labeled training data is essential for practical model deployment. Self-supervised pre-training methods, which learn representations from unlabeled data by designing pretext tasks, provide an approach to address this requirement. One inconvenience of the currently contrastive learning-based and masked image modeling-based self-supervised

Land Surface Temperature (LST) is a vital meteorological variable for assessing hydrological, ecological, and climatological dynamics, as well as energy exchanges at the land–atmosphere interface. Accurate and frequent LST measurement is essential for meteorological satellites. However, existing retrieval algorithms often fail to capture the nuances of diurnal temperature variations. This study utilizes

Tropospheric delays present a significant challenge to accurately mapping the Earth’s surface movements using interferometric synthetic aperture radar (InSAR). These delays are typically divided into stratified and turbulent components. While efforts have been made to address the stratified component, effectively mitigating turbulence remains an ongoing challenge. In response, this study proposes a

InSAR is an effective tool for indirectly monitoring large-scale hydrological-thermal dynamics of the active layer and permafrost by detecting the surface deformation. However, the conventional time-series models of InSAR technology do not consider the distinctive and pronounced seasonal characteristics of deformation over permafrost. Although permafrost-tailored models have been developed, their performance

This paper introduces a novel task of visual grounding for high-resolution synthetic aperture radar images (SARVG). SARVG aims to identify the referred object in images through natural language instructions. While object detection on SAR images has been extensively investigated, identifying objects based on natural language remains under-explored. Due to the unique satellite view and side-look geometry

Bounding box and point annotations are widely used in deep learning-based animal detection from remote sensing imagery, yet their impact on model performance and training efficiency remains insufficiently explored. This study systematically evaluates the influence of these two annotation methods using aerial survey datasets of African elephants and antelopes across three commonly employed deep learning

Due to the harsh construction environment of tunnels, the visual system must be fitted with a sphere cover of a certain thickness. The visual system with an optical sphere cover invalidates conventional measurement methods. Therefore, this paper provides a comprehensive visual measurement method using spherical glass refraction. First, the spherical glass refraction imaging is modeled using a geometry-driven

This work presents an approach to generating LiDAR point clouds with empirical intensity data on a massively parallel scale. Our primary aim is to complement existing real-world LiDAR datasets by simulating a wide spectrum of attributes, ensuring our generated data can be directly compared to real point clouds. However, our emphasis lies in intensity data, which conventionally has been generated using

Image–text retrieval (ITR) is crucial for making informed decisions in various remote sensing (RS) applications, including urban development and disaster prevention. However, creating ITR datasets that combine vision and language modalities requires extensive geo-spatial sampling, diverse categories, and detailed descriptions. To address these needs, we introduce the LuojiaHOG dataset, which is geospatially

To assess the ability of a satellite instrument to detect submerged targets, we constructed a semi-analytical relationship to link target reflectance and the contrast threshold of the satellite instrument to visibility ranges. Using numerical simulation, we found that the contrast threshold of the satellite instrument was equal to 50 % of the residual error contained in satellite Rrs data. We evaluated

Knowledge distillation (KD) has become a promising technique for obtaining a performant student detector in remote sensing images by inheriting the knowledge from a heavy teacher detector. Unfortunately, not every pixel contributes (even detrimental) equally to the final KD performance. To dispel this problem, the existing methods usually derived a distillation mask to stress the valuable regions during

The current digital elevation model super-resolution (DEM SR) methods are unstable in regions with significant spatial heterogeneity. To address this issue, this study proposes a regional DEM SR method based on an ensemble learning strategy (ELSR). Specifically, we first classified geographical regions into 10 zones based on their terrestrial geomorphologic conditions to reduce spatial heterogeneity;

Hyperspectral reflectance as well as thermal infrared emittance unmanned aerial vehicle (UAV)-borne imagery are widely used for determining plant status. However, they have certain limitations to distinguish crops subjected to combined environmental stresses such as nitrogen and water deficiencies. Studies on combined stresses would require a multimodal analysis integrating remotely sensed information

Identifying minerals on Mars is crucial for finding evidence of water on the planet. Currently, spectral inversion methods based on remote sensing data are primarily used; however, they only provide sparse and scattered maps of mineral exposures. To address this limitation, we propose a multi-scale spatial association modeling framework (MSAM) that couples the geographical distribution of Martian hydrous

Radiometric normalization (RN), also known as relative radiometric correction, is usually utilized for multi-temporal optical remote sensing image pairs. It is crucial to applications including change detection (CD) and other time-series analyses. Nevertheless, the variations across multi-temporal remote sensing image pairs are complex, containing true changes of landcover and fake changes caused by

Tree ring width (TRW) is crucial for assessing biomass increments, carbon uptake, forest productivity, and forest health. Due to the limitations involved in measuring TRW, utilizing canopy attributes based on vegetation indices (VIs) offers a promising alternative. This study investigated the species-specific relationship between the VIs derived from the Sentinel optical (Sentinel-2) and SAR (Sentinel-1)

Synthetic Aperture Radar (SAR) image characteristics are highly susceptible to variations in the radar operation condition. Meanwhile, acquiring large amounts of SAR data under various imaging conditions is still a challenge in real application scenarios. Such sensitivity and scarcity bring an inadequately robust feature representation learning to recent data-hungry deep learning-based SAR Automatic

Accurate state estimation is crucial for autonomous navigation in unmanned systems. While traditional visual and lidar systems struggle in adverse conditions such as rain, fog, or smoke, millimeter-wave radar provides robust all-weather localization and mapping capabilities. However, sparse and noisy radar point clouds often compromise localization accuracy and lead to odometry immanent drift. This

Addressing data gaps in meteorological radar scan regions remains a significant challenge. Existing radar data recovery methods tend to perform poorly under different types of missing data scenarios, often due to over-smoothing. The actual scenarios represented by radar data are complex and diverse, making it difficult to simulate missing data. Recent developments in generative models have yielded

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