Information on the rice agricultural system, including not only planting area but also phenology and cropping intensity, is critical for advancing our understanding of food and water security, methane emissions, carbon and nitrogen cycles, and avian influenza transmission. However, existing efforts primarily focus on mapping planting area and lack a comprehensive picture of the rice agricultural system

Complex cropping patterns with crop diversity are an underexploited treasure for global food security. However, significant methodological and dataset gaps in fully characterizing cropland cultivated with multiple crops and rotation sequences hinder our ability to understand and promote sustainable agricultural systems. Existing crop mapping models are challenged by the deficiency of ground reference

Lunar surface mineralogy plays a crucial role in characterizing the distribution and abundance of silicate minerals, providing pivotal insights into the geological evolution of the Moon. Existing lunar mineral distribution maps are primarily derived from the calibration of Apollo and Luna sample datasets, which are predominantly older than 3.0 Ga. However, these maps lack critical constraints from

Systematic decametric resolution global mapping of vegetation biophysical variables, including fraction of absorbed photosynthetically active radiation (fAPAR), fraction of vegetation cover (fCOVER), and leaf area index (LAI), is required to support various activities, including climate adaptation, crop management, biodiversity monitoring, and ecosystem assessments. The Canada Centre for Remote Sensing

Polygonal building outlines are crucial for geographic and cartographic applications. The existing approaches for outline extraction from aerial or satellite imagery are typically decomposed into subtasks, e.g., building masking and vectorization, or treat this task as a sequence-to-sequence prediction of ordered vertices. The former lacks efficiency, and the latter often generates redundant vertices

The fusion of SAR image features from multiple views can effectively improve the recognition performance of SAR ATR tasks. However, when the number of raw samples in SAR images is limited, multiple fusions of SAR image features from different views of the same class may result in significant feature redundancy, causing overfitting of the model. To solve those problems, we propose a multiview and multi-level

Wearable laser scanning (WLS) system has the advantages of flexibility and portability. It can be used for determining the user’s path within a prior map, which is a huge demand for applications in pedestrian navigation, collaborative mapping, augmented reality, and emergency rescue. However, existing LiDAR-based global localization methods suffer from insufficient robustness, especially in complex

Existing crop type maps usually rely on extensive ground truth, limiting the potential applicability in regions without any crop labels. Unsupervised clustering offers a promising approach for crop mapping in regions lacking labeled crop samples. However, due to the high-dimensional complexity and pronounced temporal dependencies of crop time series, existing unsupervised clustering methods are inadequate

Individual Tree Detection (ITD) can automatically recognize single trees and generate large-scale individual tree maps, providing essential insights for tree-by-tree forest management. However, ITD using remote sensing data becomes increasingly challenging as data quality and spatial resolution decrease. This paper proposes a Super-Resolution (SR)-based ITD method to predict individual tree locations

Accurate and efficient estimation of crop yield is crucial for enhancing crop variety testing, optimizing cultivation practices, and supporting effective crop management to ensure sustainable production. However, remote sensing-based yield models often face limitations due to geographical variability and diverse cultivation techniques, affecting both their model accuracy and transferability. This study

In the last twenty years, Structure from Motion (SfM) has been a constant research hotspot in the fields of photogrammetry, computer vision, robotics etc., whereas real-time performance has only recently emerged as a topic of growing interest. This work builds upon the original on-the-fly SfM (Zhan et al., 2024) and presents an updated version (v2) with three new advancements to get better SfM reconstruction

The emergence of large-scale Large Language Models (LLMs), with GPT-4 as a prominent example, has significantly propelled the rapid advancement of Artificial General Intelligence (AGI) and sparked the revolution of Artificial Intelligence 2.0. In the realm of remote sensing, there is a growing interest in developing large vision language models (VLMs) specifically tailored for data analysis in this

Remote sensing image salient object detection (RSI-SOD), as an emerging topic in computer vision, has significant applications across various sectors, such as urban planning, environmental monitoring and disaster management, etc. In recent years, RSI-SOD has seen significant advancements, largely due to advanced representation learning methods and better architectures, such as convolutional neural

In recent years, three-dimensional point clouds are used increasingly to document natural environments. Each dataset contains a diverse set of objects, at varying shapes and sizes, distributed throughout the data and intricately intertwined with the topography. Therefore, regions of interest are difficult to find and consequent analyses become a challenge. Inspired from visual perception principles

A novel way of analysing IRT images of landslides using thermal dense point clouds is herein presented. The Infrared Thermo Point Cloud tool was specifically developed to adjust and homogenize the temperature range of input images prior to the realization of a dense point cloud through conventional algorithms. In this way, bias and errors arising from differences in the temperature ranges of overlapping

With the increasing availability of Earth observation data in recent years, the development of deep learning algorithms for the classification of satellite image time series (SITS) has substantially progressed. Yet, when encountering settings of lacking target labels and distinct feature variations, even the latest classification algorithms may perform poorly in transferring knowledge from a trained

Accurately understanding the local and global distribution, categories and morphological parameters of impact craters on Mars, including variations across the southern highlands, northern lowlands, equatorial region and polar zones, is crucial for revealing the geological history and environmental changes. To this end, this paper proposes a multi-task deep learning framework, GACraterNet(Geometric

Methane (CH4) stands out as the second-largest contributor to the global warming since pre-industrial era. Anthropogenic methane emissions (e.g., oil and gas, waste management, and coal mining) are the major sources of methane release and in the meantime provide an excellent opportunity for emission reduction. Regarding this, observations from satellite remote sensing paly pivotal role in methane detection

Visual simultaneous localization and mapping (vSLAM) is crucial in various applications, ranging from robotics to augmented reality. However, dynamic environments cause difficulty to vSLAM, which often relies on extracted feature points (FPs). Effectively managing FPs in dynamic environments poses a significant challenge. To address this challenge, we propose an innovative solution that leverages a

Stop-and-go laser scanning is becoming increasingly prevalent in a variety of applications, e.g., the survey of the built environment. For this, a surveyor needs to select a set of standpoints as well as the route between them. This choice, however, has a high impact on both the economic efficiency of the respective survey as well as the completeness, accuracy, and subsequent registrability of the

This study explores the visual grounding (VG) task using data from remote sensing sensors, focusing on grounding phrases or prompts. We achieve this by employing masked feature encoding to create enriched, target-specific, and semantically aware visual representations. We aim to develop an efficient grounding architecture that uses language expressions as prompts for aerial images. Despite significant

Large-scale metric-resolution land cover mapping is crucial for a detailed understanding of large areas of the Earth surface, supporting fine-scale decision-making in sectors such as agriculture, forestry, and conservation. Despite advances in remote sensing technology, this type of mapping is often limited by a lack of high-quality manually labeled data. To address this issue, here we propose a novel

Point cloud data contains abundant information besides XYZ, such as Level of Importance (LoI) and intensity. These non-spatial dimensions are also frequently used and queried. Therefore, developing an efficient nD solution for managing and querying point clouds is imperative. Previous researchers have developed PlainSFC that maps both nD points and queries into a one-dimensional Space Filling Curve

Recently, deep learning (DL) methods, particularly convolutional neural networks (CNNs)-based ones, have significantly advanced the development of synthetic aperture radar (SAR) ship instance segmentation. However, existing instance segmentation algorithms typically rely on preset candidate boxes, which are challenging to perfectly match to ships from a regression optimization perspective, limiting

Investigations on year-to-year variations in surface urban heat island intensity (ΔIs, the change in urban heat island intensity between consecutive years) are crucial for capturing the dynamics of urban climates at mid-term scales. While the patterns and underlying drivers of Is have been extensively studied, their year-to-year variability remains poorly understood, especially across global cities

Over six years of on-orbit operations, the NASA Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) has proven the value of space-based laser altimetry for monitoring the response of Earth’s surfaces to a changing climate with continuous elevation measurements. Although bathymetry is not an official science requirement for the mission, ICESat-2 has had a transformative impact on understanding nearshore

The profound impact of light pollution on both natural and human systems is well-recognized. Particularly, light pollution at the building scale is inextricably intertwined with human living and has garnered increasing attention in recent years. However, the coarse spatial resolution of nighttime light data, coupled with the inadequacy of existing methods, have precluded detailed investigation into

Laser scanners mounted on moving platforms allow for efficient large-scale 3D mapping using light detection and ranging (LiDAR). Because the laser scanner is moving with respect to the earth, its trajectory (position and orientation over time) must be known in order to georeference the scanner measurements to an earth-fixed coordinate system. This is commonly realized through integration with satellite

Road surface information is essential for applications in urban planning, disaster routing or logistics optimization and helps to address various Sustainable Development Goals (SDGS): especially SDGs 1 (No poverty), 3 (Good health and well-being), 8 (Decent work and economic growth), 9 (Industry, Innovation and Infrastructure), 11 (Sustainable cities and communities), 12 (Responsible consumption and

In this paper, we present a novel dense SLAM system based on depth-event fusion, aiming to address the challenge of online dense reconstruction in challenging environments. To achieve robust camera tracking, we devise a hybrid depth-event pose estimation framework based on random optimization, which estimates all states jointly. Notably, we introduce an innovative 3D-2D edge alignment method based

Optical change detection is limited by imaging conditions, hindering real-time applications. Synthetic Aperture Radar (SAR) overcomes these limitations by penetrating clouds and being unaffected by lighting, enabling all-weather monitoring when combined with optical data. However, existing heterogeneous change detection datasets lack complexity, focusing on single-scene targets. To address this gap

One of the key solutions to the challenge of collecting training labels for high-resolution remote sensing images is to leverage prior information from historical land cover products, which includes knowledge derived from both same- and low-resolution land cover products (relative to the targeted images). However, employing these products as training labels directly fails to yield encouraging results

Novel Class Discovery (NCD) in 3D semantic segmentation is crucial for applications requiring the ability to learn and segment previously unknown classes in point cloud data, such as autonomous driving and urban planning. Traditional 3D semantic segmentation methods often build upon a fixed set of known classes, which restricts their ability to discover classes not covered in the original training

The texture reconstruction algorithm uses multiview images and 3D geometric surface models as data sources to establish the mapping relationship and texture consistency constraints between 2D images and 3D geometric surfaces to produce a 3D surface model with color reality. The existing algorithms still have challenges in terms of texture quality when faced with dynamic scenes with complex outdoor

Ground ice melt can reshape permafrost environments, with repercussions for Northern livelihoods and infrastructure. However, fine-scale permafrost ground ice products are lacking, limiting environmental change predictions. We propose an InSAR-based approach for estimating ground ice near the permafrost table in sparsely vegetated terrain underlain by continuous permafrost. The Bayesian inversion retrieves

Vehicle detection in infrared aerial imagery is essential for military and civilian applications due to its effectiveness in low-light and adverse scenarios. However, the low spectral and pixel resolution of long-wave infrared (LWIR) results in limited information compared to visible light, causing significant background interference. Moreover, varying thermal radiation from vehicle movement and environmental

Remote-sensing (RS) image compression at extremely low bitrates has always been a challenging task in practical scenarios like edge device storage and narrow bandwidth transmission. Generative models including VAEs and GANs have been explored to compress RS images into extremely low-bitrate streams. However, these generative models struggle to reconstruct visually plausible images due to the highly

The robust and accurate matching of multimodal remote sensing images (MRSIs) is crucial for realizing the fusion of multisource remote sensing image information. Traditional matching methods fail to exhibit effective performance when confronted with significant nonlinear radiometric distortions (NRDs) and geometric differences in MRSIs. To address this critical issue, we propose a novel framework called

Oriented object detection has been rapidly developed in the past few years, but most of these methods assume the training and testing images are under the same statistical distribution, which is far from reality. In this paper, we propose the task of domain generalized oriented object detection, which intends to explore the generalization of oriented object detectors on arbitrary unseen target domains

Satellites continuously capture vast amounts of data daily, including multispectral remote sensing images (MSRSI), which facilitate the analysis of planetary processes and changes. New machine-learning techniques are employed to develop models to identify regions with significant changes, predict land-use conditions, and segment areas of interest. However, these methods often require large volumes

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