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

The Surface Water and Ocean Topography (SWOT) altimeter mission provides a significant opportunity to improve the accuracy of geoid gradients (GGs) and marine gravity fields. This paper presents a new method, namely LSA3, to determine the north and east (\(\xi \) and \(\eta \)) components of GGs from simulated and real SWOT data in the northern South China Sea. To fully use SWOT’s fine-scale sea surface

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

Increasing compound dry and hot events (CDHEs) have become a leading risk to regional and global crop production. While numerous accounts of crop growth or yield response to climate extremes exist, impacts of CDHEs on crop yield are not well quantified, especially for that associated with large-scale modes of climate variabilities. Here we address this issue by using statistical maize yield data over

The effects of aerosols on carbon sequestration in sunlit and shaded leaves of vegetation cover are well understood; however, it is difficult to quantify these effects on gross primary productivity (GPP) with existing observational data. In this study, the Community Earth System Model (CESM) is used to quantify the effects of aerosol emissions on the GPP of sunlit and shaded leaves in Northern Hemisphere

There is a global chert accumulation event during the Permian period - Permian Chert Event (PCE, ∼280 Ma to 251 Ma), indicating large perturbations in the marine silicon cycle. The Permian also witnessed termination of Earth's penultimate icehouse (late Paleozoic ice age - LPIA, ∼340 Ma to ∼260 Ma), final assembly of the Pangea supercontinent and significant changes in biosphere. However, few studies

Climate change has profound implications for the social stability and societal crisis. The prominent Wanli megadrought (1585–1590 CE) had a profound impact on northern China and might contribute to the collapse of the Ming Dynasty. Despite its historical significance, the underlying mechanisms of the Wanli megadrought remain unclear. This study combines reconstruction and CESM Last Millennium Ensemble

Driving off‐highway vehicles (OHVs), which contributes to habitat degradation and fragmentation, is a common recreational activity in the United States and other parts of the world, particularly in desert environments with fragile ecosystems. Although habitat degradation and mortality from the expansion of OHV networks are thought to have major impacts on desert species, comprehensive maps of OHV route

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

In this contribution, we introduce some new theory for the classical GNSS ambiguity function (AF) method. We provide the probability model by means of which the AF-estimator becomes a maximum likelihood estimator, and we provide a globally convergent algorithm for computing the AF-estimate. The algorithm is constructed from combining the branch-and-bound principle, with a special convex relaxation

The weathering of carbonate rocks releases considerable amounts of cadmium (Cd) thus posing potential risks to the ecosystem and human health. The biogeochemical cycling of Cd during weathering processes is poorly understood because of the absence of efficient tracers for Cd biogeochemical processes. In this study, the Cd isotope technique was applied to elucidate the redistribution and fate of Cd

Silicate weathering acts as a negative feedback that regulates the Earth's long-term climate by removing CO2 from the atmosphere. However, maintaining a close balance in the global carbon cycle requires a timely response of weathering to the changing climate. Here, we investigate major and trace element contents of both silt (<63 μm) and clay (<2 μm) fractions of surface sediments collected at high

The Carnian Pluvial Episode (CPE, Late Triassic, ∼234–232 Ma) was a major climate change characterized by intensified hydrological cycle, perturbations in the carbon cycle, global warming and ocean anoxia. The climate perturbation was coeval to significant biological changes, including extinctions and the emergence of new clades. Research on the climatic changes of the CPE has been conducted extensively

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

The Ediacaran strata of Newfoundland, Canada (580–560 Ma) record some of the first animal communities. The in-situ preservation of these sessile organisms means that the positions and sizes of specimens on the bedding planes encapsulate their life-histories, enabling spatial analyses to reconstruct their ecological dynamics. However, it is not known how these Ediacaran ecological dynamics vary across

Using continuous seismic data recorded by the broadband ocean bottom seismometer network of the Dense Ocean-floor Network system for Earthquakes and Tsunamis (DONET), this study examines the energy trends of infragravity (IG) waves propagating across the Pacific Ocean. The DONET stations, located in the Nankai seismogenic zone off the southern coast of Japan, are deployed at water depths ranging from

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

Savannas, defined by a balance of woody and herbaceous vegetation, are vital for global biodiversity and carbon sequestration. Yet, their stability is increasingly at risk due to climate change and human impacts. The responses of these ecosystems to extreme drought events remain poorly understood, especially in relation to the regional variations in soil, terrain, climate history and disturbance legacy

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

Deciphering Earth's surface denudation—encompassing both physical erosion and chemical weathering—is essential for understanding sediment flux and its impact on long-term carbon cycle. However, quantifying denudation and chemical weathering across different timescales remains challenging. Here, we combine the covariation of different fluxes derived from in-situ 10Bein (quartz, 250–500 μm), the ratio

Chemical weathering of differential lithologies is the primary factor causing redistribution of the elements chromium (Cr) and zinc (Zn) in Earth's surface. Clay-sized fractions (< 2 μm, CSFs) as the ubiquitous weathering products play a critical role in hosting Cr and Zn. However, currently inadequate knowledge of CSFs from sedimentary rocks to soils might hamper a clear understanding regarding Cr

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

It is essential to quantify the frequency and geometric distribution of tropical cyclone genesis (TCG) for assessing tropical cyclone (TC) activities and associated hazards, especially in the context of climate change. Despite the fruitful achievements in TCG studies typically via empirical-indices based methods and statistical techniques, it is still challenging to estimate TCGs accurately and flexibly

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

This paper presents a study on depth modernization, paralleling height modernization for land elevations. Depth modernization integrates mean sea surface (MSS) models, ocean tide models, and precise ship positioning to achieve accurate seafloor depth measurements. Conventional methods rely on tidal corrections and chart datum from temporary tide gauges, which can be challenging in regions with complex

The Qinghai-Tibet Plateau has undergone significant anthropogenic activities and climate change impacts over the past years, with these trends projected to intensify. These changes are likely to alter the alpine grassland ecosystem structure and services. To assess the impacts on carbon storage (CS), we employed an analytical framework integrating the optimal parameter-based geographical detector (GD)

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

The consumption of atmospheric CO2 as a result of silicate weathering is a very important carbon sink process over geological time scales, serving as a significant mechanism for regulating global carbon cycling and climate. However, whether silicate weathering is a positive driving factor for climate change or a negative feedback to mitigate climate change is still unclear, mainly due to a lack of

The mechanisms that enable the Earth system to maintain habitability are much debated. The influences of active tectonic uplift and the temperature feedback are proposed. The temperature negative feedback mechanism is considered one of the key processes preventing excessive CO₂ consumption. However, the specific mechanisms by which temperature variations during the Cenozoic influenced silicate weathering

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 Tibetan Plateau (TP) vegetation exhibits high sensitivity to climate fluctuations and disturbances, making it a focal area for studying alpine ecosystem dynamics. Normalized difference vegetation index (NDVI), as an indicator of vegetation growth and health status, is one of the most effective satellite-based vegetation parameters that are used to monitor vegetation dynamics. However, long-term

The multidecadal variability of Atlantic Meridional Overturning Circulation (AMOC) significantly impacts various climate phenomena, yet the role of ocean surface freshwater flux in this variability is not fully understood. Through experiments with and without surface freshwater flux adjustment under pre-industrial conditions based on the Community Earth System Model version 1.0, we demonstrate that

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

The peak of the Late Paleozoic Ice Age (LPIA) coincided with atmospheric carbon dioxide (pCO2) lows, and large-magnitude fluctuations in glacial-interglacial pCO2, but the driver(s) for pCO2 drawdown at this time remain debated. Backed by parameters for the frequency and magnitude of Late Carboniferous volcanism derived from the rock record, we apply an intermediate complexity Earth system model to

Root zone soil moisture (RZSM) plays a critical role in numerous ecological and environmental processes and holds significant importance for agriculture, hydrology, and climate studies. Although it can be estimated by hydrology or land surface models, the accuracy of such estimations is often limited. Data fusion offers a promising approach to improving RZSM estimation accuracy, yet few studies have

In the context of global climate change, changes in water conditions significantly affect the productivity of terrestrial ecosystems. However, the impact of previous water conditions has not received sufficient attention. Using machine learning and a large amount of global ground observation data, we evaluated the impact of previous water conditions at various time scales on global productivity estimation