High-resolution topographic mapping is essential for scientific investigations and operational exploration of planets, such as Mars. Photoclinometry, which uses light scattered from a surface to reconstruct 3D topography, can retrieve subtle topographic details from monocular images. However, its performance is affected by the atmosphere of Mars, which alters surface reflectance mechanisms due to the

Urban Physical Disorder (UPD), such as old or abandoned buildings, broken sidewalks, litter, and graffiti, has a negative impact on residents’ quality of life. They can also increase crime rates, cause social disorder, and pose a public health risk. Currently, there is a lack of efficient and reliable methods for detecting and understanding UPD. To bridge this gap, we propose UPDExplainer, an interpretable

This paper presents an end-to-end solution, the Spectral Filter Array (SFA)-guided Mosaic Transformer (SMT), designed for tracking small objects within mosaic spectral videos captured by snapshot spectral cameras. Tracking small objects amidst complex scenes poses greater challenges due to their variable appearances and limited feature representation. Spectral imaging, leveraging spectral and spatial

The completion of point cloud directly affects the accuracy of primitive parameter extraction and 3D reconstruction. Due to the limitations of sensor scanning, point cloud are often incomplete due to occlusions during the data collection. To solve the problem of incomplete building point clouds and to support geometric detail LoD2 reconstruction, a point cloud completion method with structural constraint

Pan-sharpening is a very productive technique to enhance the spatial details of multispectral images with the aid of panchromatic images. Nowadays, deep learning-based pan-sharpening has scored tremendous achievements. However, strict requirement for training image pairs and low generalization hamper the development of supervised pan-sharpening with limited samples. Unsupervised image fusion is an

Airborne LiDAR bathymetry allows an efficient and area-wide measurement of the water bottom topography in shallow waters. However, the maximum water depth range of this method is mainly limited by water turbidity, resulting in a reduced coverage of the water bottom topography in deeper waters. Water turbidity causes attenuation effects and hampers the reliable detection of water bottom echoes in the

Multimodal image matching is a core basis for information fusion, change detection, and image-based navigation. However, multimodal images may simultaneously suffer from severe nonlinear radiation distortion (NRD) and complex geometric differences, which pose great challenges to existing methods. Although deep learning-based methods had shown potential in image matching, they mainly focus on same-source

Reliance on vast annotations to achieve leading performance severely restricts the practicality of large-scale point cloud semantic segmentation. For the purpose of reducing data annotation costs, effective labeling schemes are developed and contribute to attaining competitive results under weak supervision strategy. Revisiting current weak label forms, we introduce One Class One Click (OCOC), a low

Single-image 3D urban building reconstruction is an important and challenging topic in computer vision. However, it is a highly ill-posed problem due to the intrinsic geometrical ambiguity in a 3D space. To address the problem, this paper presents a novel single-image piecewise planar reconstruction method based on geometric priors. The proposed method utilizes geometric priors (e.g., plane orientation

Satellite retrievals of thermal infrared data from the Earth’s surface when combined with the surface energy balance equation are widely used to monitor land surface evapotranspiration (ET). Unfortunately thermal infrared based models using this approach have a systematic positive bias with ET during soil water stress conditions. The Two-source Energy Balance model using information on Soil Moisture

Safety is critical for autonomous driving, and one aspect of improving safety is to accurately capture the uncertainties of the perception system, especially knowing the unknown. Different from only providing deterministic or probabilistic results, e.g., probabilistic object detection, that only provide partial information for the perception scenario, we propose a complete probabilistic model named

In this paper, we propose a practical three-dimensional (3D) real-scene reconstruction framework named Deep3D, which is paired with a deep learning based multi-view stereo (MVS) matching model named the adaptive multi-view aggregation matching (Ada-MVS) model, to obtain a 3D textured mesh model from multi-view oblique aerial images. Deep3D is the first deep learning based framework for 3D scene reconstruction

RGBT person detection benefits numerous vital applications like surveillance, search, and rescue. Meanwhile, drones can capture images holding broad perspectives and large searching regions per frame, which can notably improve the efficacy of large-scale search and rescue missions. In this work, we leverage the advantages of drone-based vision for RGBT person detection. The drone-based RGBT person

The continuous dynamic of surface urban heat island (SUHI) effect is highly needed in urban climate studies. However, temporal variations of SUHI intensity (SUHII) have been understudied in previous studies owing to the lack of spatially seamless and temporally continuous land surface temperature (LST) data, particularly in urban domains. Also, the relationship between SUHII variations and heterogeneous

3D point cloud panoptic segmentation is the combined task to (i) assign each point to a semantic class and (ii) separate the points in each class into object instances. Recently there has been an increased interest in such comprehensive 3D scene understanding, building on the rapid advances of semantic segmentation due to the advent of deep 3D neural networks. Yet, to date there is very little work

Change detection methods based on Earth Observations are increasingly used to monitor rainforest in the intertropical band. Until recently, deforestation monitoring was mainly based on remotely sensed optical images which often face limitations in humid tropical areas due to frequent cloud coverage. This leads to late detections of disturbance events. Since the launch of Sentinel-1 acquiring images

RGB-D sensors can be a low-cost solution for an accurate silo’s content monitoring which is fundamental for its efficient management. Some reference information such as the position and orientation of the sensor with respect to the silo’s geometry is fundamental for obtaining correct content measurements from acquired data. Since in real cases this information is not always known, a new method to obtain

Previous studies have shown that Synthetic Aperture Radar (SAR) is able to detect burned areas, serving as a key data source for monitoring active wildfires in situations where optical sensors are hindered by dense smoke or cloud cover. Radar remote sensing provides rich and useful data on historical burned areas, which are critical for large-scale wildfire burned area mapping. This study aims to unveil

Line segment matching (LSM) plays an important role in image matching, while it is always a challenging problem due to line fracture and high geometric complexity. In this paper, we propose a line segment mismatch removal to address the sensitivity to segment length and the fracture problem. The mismatch removal removes the massive false matches obtained by the descriptors like LBD. Specifically, we

Thermal SLAM outperforms visual SLAM under conditions of nighttime, low visibility (e.g., fog, smoke, and dust), and direct glare. However, the unique non-uniformity correction for thermal imaging will cause data interruption, and the weak texture of the thermal image itself will lead to poor localization accuracy. To overcome these limitations, this paper presents a monocular thermal camera-based

The use of artificial intelligence has led to an increase in road extraction projects from satellite images through deep learning. However, multi-spectral images (MSI) have been largely overlooked in road extraction algorithms due to their lower resolution compared to panchromatic or fused images. Additionally, deep learning faces the challenge of image content reasoning from distant contexts in data

Rapid and accurate agricultural parcel mapping from high-resolution remote sensing imagery is fundamental to precision agriculture for smallholder farming systems. However, due to the narrow and small-size parcels, and the significant spatio-spectral variability, the existing two-stage segmentation methods cannot extract individual parcels automatically. In this article, the end-to-end vectorization

Accurate characterization of plant phenology is of great importance for monitoring global carbon, water, and energy cycling. Remotely sensed satellite observations have been widely used to estimate land surface phenology across multiple spatial scales in the last three decades. Recent development on satellite solar-induced chlorophyll fluorescence (SIF) observations have opened an opportunity to monitor

The dynamics of built-up height are a crucial aspect of urban form, enabling the characterization of urban growth patterns and the attainment of sustainable development goals. While past studies have focused on urban extent mapping, little has been done to reveal changes in vertical structures in built-up areas. In this study, we reconstructed annual urban form dynamics in Beijing, China, from 1990

The contextual surface temperature-vegetation index (TVX) models have been widely used for the retrieval of soil moisture (SM) and evapotranspiration (ET). One of the key premises for their application is to determine quantitatively the theoretical boundaries of this contextual TVX space. Usually these theoretical boundaries are determined based on land surface energy balance principle. Although sound

Although current methods based on deep learning (DL) have been widely employed in shadow detection tasks, the cluttered background and complex shadow features in remote sensing images (RSIs) make shadow detection still a challenging task. In this paper, we apply a neural network combined with a distance transformation algorithm to RSI shadow detection for the first time and propose a novel slice-to-slice

Abstract not available

Mapping cultural significance of heritage properties in urban environment from the perspective of the public has become an increasingly relevant process, as highlighted by the 2011 UNESCO Recommendation on the Historic Urban Landscape (HUL). With the ubiquitous use of social media and the prosperous developments in machine and deep learning, it has become feasible to collect and process massive amounts

The precise classification of land covers with hyperspectral imagery (HSI) is a major research-focused topic in remote sensing, especially using unmanned aerial vehicle (UAV) systems as the abundant data sources have brought severe intra-class spectral variability and high spatial heterogeneity challenges, making precise classification difficult. To this end, a novel three-dimensional singular spectrum

Synthetic aperture radar (SAR) remote sensing technology has the advantage of all-weather observation and can acquire time-series images with crop growth period, which has great potential for applications such as crop phenology analysis. However, available studies primarily focus on conducting statistical and crop growth analyses based on the polarization or backscatter intensities of SAR images, and

Wetlands are one of the most important ecosystems on the Earth, and using hyperspectral remote sensing (RS) technology for fine wetland mapping is important for restoring and protecting the natural resources of coastal wetlands. However, the high cost in collecting labeled samples and inconsistent acquisition conditions across different geographic regions or scenes lead to difficulties in wetland mapping

A timely and accurate spatial mapping of built-up areas (BA) is crucial in making cities and human settlements safe, resilient, and sustainable. Synthetic Aperture Radar (SAR) data are useful for BA mapping due to strong coherent backscatter from diverse human-made targets, distinct texture patterns, and sensitivity to its geometric characteristics. However, BA mapping using SAR data is still challenging

Knowledge of actual crop transpiration (T) is important for advanced crop management but challenging to obtain due to the large spatial and temporal variation of T. Remote sensing offers various possibilities to assess T dynamics, while particularly sun-induced chlorophyll fluorescence (SIF) has been demonstrated as a sensitive empirical proxy for T. Despite this success, the advancement of the mechanistic

Digital mapping of soil organic carbon (SOC) is essential for visualizing the spatial distribution at different regions and scales. However, existing studies using remote sensing are limited by the low spectral resolution of multispectral data for accurate estimation of SOC, and the failure of laboratory visible and near-infrared and shortwave infrared (Vis-NIR) spectroscopy to perform pixel-level

Spatiotemporal fusion (STF) is considered an effective way to address the mutual constraints of the spatiotemporal resolutions of the remote sensing images from a single satellite sensor. Although the deep learning (DL)-based STF methods have shown great potential so far, there are still some deficiencies. Supervised DL-based methods usually train the network on the original scale data with richer

Influenced by the imaging mechanism, the occurrence of interference clutter in synthetic aperture radar (SAR) renders the identification of false alarms using detectors challenging. Polarimetric SAR has the potential to improve ship detection performance owing to its distinctive polarization characteristics. The present study proposes a dualistic cascade convolutional neural network (DCCNN) algorithm

Land surface phenology (LSP) is beneficial to understand ecosystem response to climate change, vegetation and crop type discrimination, and ecological modeling. However, the existing efforts based on coarse resolution data (≥500 m) cannot perform well in regions with higher spatial heterogeneity and multi-cropping system, such as China. Given that the majority of 10 m/30 m-based phenological research

Place recognition is an important branch of Simultaneous Localization and Mapping (SLAM), which finds revisited places, thereby reducing error accumulations. Most mainstream methods only concentrate on the similarity of two LiDAR scans and cannot obtain the pose information. In this paper, we design a descriptor named Occupied Place Description (OPD) and propose an efficient map-aided place recognition

Accurately orientated and matched images and light detection and ranging (LiDAR) point clouds are becoming the new standard for many applications in geomatics. Large data volumes and different observation types make scalability and efficient optimization challenging, particularly for matching based on variants of the iterative closest point algorithm. Here, we present a method to address this by temporally

Drought has a serious impact on the health of terrestrial ecosystems, socio-economy and human health. It is important to accurately monitor drought conditions. Soil moisture (SM) can directly characterize surface dryness and wetness. Precipitation (PPT), evapotranspiration (ET), land surface temperature (LST), and vegetation status indirectly relate to SM, and they influence each other. Therefore,

The bi-temporal change detection (CD) is still challenging for high-resolution optical remote sensing data analysis due to various factors such as complex textures, seasonal variations, climate changes, and new requirements. We propose an attention-based multiscale transformer network (AMTNet) that utilizes a CNN-transformer structure to address this issue. Our Siamese network based on the CNN-transformer

The leaf area index (LAI) retrieval methods based on traditional neural networks require a large number of training samples constructed from remote sensing data or simulation data using radiative transfer models. Furthermore, the training samples for the neural networks are sensor-specific. Therefore, the existing training samples for a sensor cannot be directly applied to estimate LAI values from

Satellite monitoring is an effective way to obtain the spatiotemporal information for surface water resources. However, due to problems such as clouds, cloud shadows, and sensor failures, there many gaps in satellite images, posing challenges for the accurate monitoring of surface water changes. Here, a new image-similarity-based gap-filling method using Landsat images, to obtain gapless surface water

Nearshore sediment distribution is a significant component of fundamental geographic data, but the inherent optical properties of the water column make the large-scale observation of sediment particularly difficult. The development of satellite-based imaging sensors has enabled earth observation to capture more types and finer granularities of ground information, making it possible to obtain the target

Wildfires have significant impacts on the environment, society, and economy. Consequently, understanding its dynamics is crucial to evaluate such effects. Nonetheless, monitoring and measuring the burned area by traditional, non-automatic methods remains time-consuming and challenging. For several years, automatic semantic segmentation models have been used to describe natural phenomena, but deep learning

The Japan Aerospace Exploration Agency (JAXA) launched the Global Change Observation Mission - Climate (GCOM-C) satellite on December 23rd, 2017. As a part of the standard products of JAXA’s GCOM-C satellite, we developed the Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) product. In comparison to other global LAI and FAPAR products by the National Aeronautics

Accurate and timely information on winter wheat spatial distribution is essential for food security and environmental sustainability. However, high-quality nation-wide winter wheat products at high resolutions are still scarce around the world, and the approaches for winter wheat mapping are generally constrained by the lack of sufficient and representative training data. In this study, a knowledge-based

Generating learning-friendly representations for points in space is a fundamental and long-standing problem in machine learning. Recently, multi-scale encoding schemes (such as Space2Vec and NeRF) were proposed to directly encode any point in 2D or 3D Euclidean space as a high-dimensional vector, and has been successfully applied to various (geo)spatial prediction and generative tasks. However, all

Photorealistic 3D models are important data sources for digital twin cities and smart city applications. These models are usually generated from data collected by aerial or ground-based platforms (e.g., mobile mapping systems (MMSs) and backpack systems) separately. Aerial and ground-based platforms capture data from overhead and ground surfaces, respectively, offering complementary information for

Deep learning-based camera pose regression approaches have achieved outstanding performance for visual indoor localisation. However, these approaches are limited by the availability of images with known camera poses, and they often require a comprehensive mapping of the indoor scenes, which is labour-intensive and often impractical. Recent studies have shown that synthetic images derived from simple

Despite the good results that have been achieved in unimodal segmentation, the inherent limitations of individual data increase the difficulty of achieving breakthroughs in performance. For that reason, multi-modal learning is increasingly being explored within the field of remote sensing. The present multi-modal methods usually map high-dimensional features to low-dimensional spaces as a preprocess