Interpreting the similarities and dissimilarities in spatiotemporal variations and various controls between surface and canopy urban heat islands (UHIs) is critical for a better understanding of their vertical structure. Preceding comparisons of the surface UHI (SUHI) and canopy UHI (CUHI), however, remain mostly restricted either in a single city or over a few cities within limited background climates;

Semantic segmentation of remotely sensed imagery plays a critical role in many real-world applications, such as environmental change monitoring, precision agriculture, environmental protection, and economic assessment. Following rapid developments in sensor technologies, vast numbers of fine-resolution satellite and airborne remote sensing images are now available, for which semantic segmentation is

Detecting the arbitrary-oriented objects in aerial images is a significant yet challenging task in remote sensing image analysis. Existing methods generally use the oriented bounding boxes (OBBs) as Region of Interests (RoIs) to detect aerial objects, and learn the orientation angle information of objects under the supervision of OBB annotations. However, directly learning the orientation angle suffers

Sun glint contamination is a significant problem for high-resolution remote sensing over aquatic environments. Sun glint is a particular issue for researchers using aerial imagery to assess shallow water benthic communities, as it may be wrongly classified as benthic substrates of interest. Although various methods are available to correct for sun glint using multispectral and hyperspectral imagery

The 3D reconstruction of objects is a prerequisite for many highly relevant applications of computer vision such as mobile robotics or autonomous driving. To deal with the inverse problem of reconstructing 3D objects from their 2D projections, a common strategy is to incorporate prior object knowledge into the reconstruction approach by establishing a 3D model and aligning it to the 2D image plane

Line segment matching for wide-baseline images is challenging due to the significant viewpoint differences. In this study, we propose a hierarchical line segment matching method based on viewpoint robust local structure and geometric constraints. In our approach, line segments are paired and classified into three types representing heuristically those with different level of matchability: structured

Digital surface models (DSM) generated from multi-stereo satellite images are getting higher in quality owing to the improved data resolution and photogrammetric reconstruction algorithms. Very-high-resolution (VHR, with sub-meter level resolution) satellite images effectively act as a unique data source for 3D building modeling, because it provides a much wider data coverage with lower cost than the

This paper presents a road detection and tracking technique using polarization characteristics of the road in the long-wave infrared (LWIR) spectrum. Conventional vision-based road detection techniques often apply color and texture information, which tend to underperform in low illumination conditions at night. The Division of Focal Plane (DoFP) infrared polarization imaging technology enables real-time

Accurate seismic risk modeling requires knowledge of key structural characteristics of buildings. However, to date, the collection of such data is highly expensive in terms of labor, time and money and thus prohibitive for a spatially continuous large-area monitoring. This study quantitatively evaluates the potential of an automated and thus more efficient collection of vulnerability-related structural

Road detection based on convolutional neural networks (CNNs) has achieved remarkable performances for very high resolution (VHR) remote sensing images. However, this approach relies on massive annotated samples, and the problem of limited generalization for unseen images still remains. The manual pixel-level labeling process is also extremely time-consuming, and the performance of CNNs degrades significantly

Vehicle detection in Unmanned Aerial Vehicle (UAV) imagery plays a crucial role in a variety of applications. However, UAVs are usually small, very maneuverable, and can take images from a variety of viewpoints and heights, leading to large differences in vehicle appearance and size. To address the vehicle detection challenge with such diversity in UAV images, we seek to align features between different

Point cloud registration is a fundamental problem in 3D computer vision. This paper addresses scan matching for reliable Mobile LiDAR Systems (MLSs) that can traverse different environments and be robust against outliers and noise. High reliability in multiple scenarios is vital to many applications, such as autonomous driving, but poor feature representations often compromise it. This paper introduces

Countrywide winter and summer Sentinel-1 (S1) backscatter data, cloud-free summer Sentinel-2 (S2) images, an Airborne Laser Scanning (ALS)-based Digital Terrain Model (DTM) and a forest mask were used to model and subsequently map Dominant Leaf Type (DLT) with the thematic classes broadleaved and coniferous trees for the whole of Switzerland. A novel workflow was developed that is robust, cost-efficient

Atmospheric noise is one of the primary challenges for improving the accuracy of deformation estimation by InSAR technologies. Temporal filtering methods, like Gaussian filtering, are commonly used to remove the atmospheric noise from the InSAR time series. Such low-pass filters can effectively suppress stochastic noise. Yet, its performance heavily depends on the parameter settings and can easily

We develop a new statistically-robust adaptive regression method (SARM) to extract clear sky true color imagery, approximating the near-surface imagery, derived from multiple satellite daily imagery time series, while avoiding artifacts due to clouds and cloud shadows. We compare the SARM-derived near-surface imagery against simpler approaches for various surface types, and perform a quantitative evaluation

Road markings are of great significance to road inventory management, intelligent transportation systems, high-definition maps (HD Maps), and autonomous driving. Most existing methods focus on extracting and classifying the road markings from mobile laser scanning (MLS) point clouds. Nevertheless, the performance suffers from the wear and incompleteness of road markings. Converting the extracted road

Identification of flood water extent from satellite images has historically relied on either synthetic aperture radar (SAR) or multi-spectral (MS) imagery. MS sensors are limited to cloud free conditions, whereas SAR imagery is plagued by noise-like speckle. Prior studies that use combinations of MS and SAR data to overcome individual limitations of these sensors have not fully examined sensitivity

Sentinel-3 is a newly launched satellite implemented by the European Space Agency (ESA) for global observation. The Ocean and Land Colour Imager (OLCI) sensor onboard Sentinel-3 provides 21 band images with a fine spectral resolution and is of great value for ocean, land and atmospheric monitoring. The two platforms (Sentinel-3A and -3B) can provide OLCI images at an almost daily temporal resolution

In this paper, we propose a novel multi-manifold feature fusion (MMFF) based deep learning framework for automatic target recognition (ATR) in complex-valued synthetic aperture radar (SAR) images. The conventional real-valued convolutional neural networks (CNN) usually ignore the intrinsic geometry structure of complex numbers, and the existing complex- and manifold-valued CNNs are not in accordance

Existing supervised-learning-based methods have achieved remarkable performance for Airborne LiDAR point cloud classification with the help of large-scale human-annotated datasets. However, human annotations are usually labor-intensive and time-consuming, especially for dense segmentation labels, and an inadequate dataset may lead to poor generalization ability of the learned models. In this paper

This paper addresses appearance based domain adaptation for the pixel-wise classification of remotely sensed data using deep neural networks (DNN) as a strategy to reduce the requirements of DNN with respect to the availability of training data. We focus on the setting in which labelled data are only available in a source domain DS, but not in a target domain DT, known as unsupervised domain adaptation

The positioning of laser scanners for indoor surveying is still a time and cost expensive process. This article proposes an optimization approach for computing an admissible sensor placement with the minimal number of sensor view point positions. The approach facilitates both wall and floor surveying based on a floorplan of the study object. Optimal solutions are calculated by solving an Integer Linear

High spatial resolution bathymetric maps of coral reefs can show the details of terrain. However, most satellite-based imagery with a spatial resolution < 10 m has only three visible bands and one near-infrared (NIR) band. When in situ bathymetric data are unavailable, it is difficult to map bathymetry from high spatial resolution imagery with spectral matching or empirical models for clear or turbid

To solve problems in 3D modelling, including unclear 3D edges in point clouds, lack of geometric semantics, confusing topological relations between 3D models, and low degree of automation in traditional model-driven 3D modelling, a method for high-accuracy and automatic monomer 3D modelling must be developed. This paper is the first to propose a 3D modelling strategy, called combining data-and-model-driven

Staring spotlight images with a spatial resolution of up to tens of centimeters are good data sources for urban applications including displacement mapping. However, phase discontinuities, layover, and shadowing effect are also associated with staring spotlight interferograms, adding to the difficulties in height estimation and spatial phase unwrapping. The scattering mechanism of the staring spotlight

Vegetation phenology can be extracted from vegetation index (VI) time series of satellite data. The maximum value composite (MVC) procedure and smoothing filters have been conventionally used as standard methods to exclude noises in the VI time series before extracting the vegetation phenology [e.g., National Aeronautics and Space Administration (NASA) VNP22Q2 and United States Geological Survey (USGS)

The identification and treatment of mining-induced ground fissures are of great significance to mine safety and ecological and environmental protection. In this study, a novel method for ground fissure identification and exploration by infrared remote sensing onboard an unmanned aerial vehicle (UAV) was proposed. Using this method, a region of interest (ROI) that includes ground fissures directly above

Although deep learning has revolutionized remote sensing (RS) image scene classification, current deep learning-based approaches highly depend on the massive supervision of predetermined scene categories and have disappointingly poor performance on new categories that go beyond predetermined scene categories. In reality, the classification task often has to be extended along with the emergence of new

Atmospheric correction (AC) is a key step in ocean color remote sensing for obtaining oceanic and coastal water ecosystem products. However, traditional NIR-based AC scheme (ACNIR) often fails in turbid coastal waters dominated by high suspended particulate matter (SPM). SWIR-based AC is valid for turbid waters, but it cannot be applied to some satellite sensors (e.g., Sea-viewing Wide Field-of-view

Recent developments in data acquisition technology allow us to collect 3D texture meshes quickly. Those can help us understand and analyse the urban environment, and as a consequence are useful for several applications like spatial analysis and urban planning. Semantic segmentation of texture meshes through deep learning methods can enhance this understanding, but it requires a lot of labelled data

Coastal wetland mapping is often difficult because of the heterogeneous vegetation compositions and associated tidal effects. In this study, we employed the U-Net and developed an adaptive deep learning approach to map statewide salt marshes in estuarine emergent wetlands of South Carolina (SC), USA, from 20 Sentinel-2A&B images. Considering the spatial heterogeneity of the coastal environment, two

Globally, olive (Olea europaea L.) productivity is threatened by plant pathogens, particularly the fungus Verticillium dahliae (Vd) and the bacterium Xylella fastidiosa (Xf). Infections by these pathogens restrict water and nutrient flow through xylem, producing a similar set of symptoms that can also be confounded with water stress. Conventional in situ monitoring techniques are time consuming and

Live fuel moisture content (LFMC) is an essential variable to model fire danger and behaviour. This paper presents the first application of deep learning to LFMC estimation based on the historical LFMC ground samples of the Globe-LFMC database, as a step towards operational daily LFMC mapping in the Contiguous United States (CONUS). One-year MODerate resolution Imaging Spectroradiometer (MODIS) time

Surface water is experiencing significant changes due to anthropogenic activities and climate change. Recent studies have explored long-term surface water dynamics with the development of multi-source remote sensing data and mapping technology. However, cost-effective and time-saving reliable references for mapping surface water from remote sensing images at multiple times are still lacking. Further

Scene classification is a means to interpret high-resolution remote sensing (HRS) imagery, to obtain the high-level semantic information, which can provide a reliable reference for urban planning and monitoring. The traditional scene classification methods based on HRS imagery take uniform grid cells as the scene units, thereby missing the geographic boundaries and leading to the mosaicking effect

With the development of space-based imaging technology, a larger and larger number of images with different modalities and resolutions are available. The optical images reflect the abundant spectral information and geometric shape of ground objects, whose qualities are degraded easily in poor atmospheric conditions. Although synthetic aperture radar (SAR) images cannot provide the spectral features

Terrestrial laser scanners are widely used to derive unbiased and non-destructive estimates of the vertical distribution of the plant area index and plant area volume density at plot-level scales, as well as the above-ground biomass, height, and diameter at breast height of individual trees. Multiple scans are often employed to capture and register data so that all of the stems can be detected and

Satellite imaging spectroscopy for terrestrial applications is reaching maturity with recently launched and upcoming science-driven missions, e.g. PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Environmental Mapping and Analysis Program (EnMAP), respectively. Moreover, the high-priority mission candidate Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) is expected

Knowledge about the spatial and temporal distribution of exposed soils is necessary for e.g., soil erosion mitigation. Earth Observation (EO) is a valuable data source for detecting exposed soils on a large scale. In the last couple of years, the multitemporal compositing technique has been used for the generation of so-called exposed soil composites that overcome the limitation of temporarily coverage

Reliable urban road vector maps are essential for urban analysis because the spatial distribution of road networks reflects urban development under the combined effects of nature and socio-economics. Diverse very high resolution (VHR) remote sensing images are now available, enabling explicit extraction of urban road vector maps over wide areas. Urban road vectorization mapping consists of two separate

Accurate paddy rice mapping with remote sensing at a regional scale plays critical roles in agriculture and ecology. Previous studies mainly employed a single key phenological period (i.e., transplanting) for paddy rice mapping. However, the prominent poor spectral separability between paddy rice and others (e.g., wetland vegetation) exists in this period. To this end, we developed an enhanced pixel-based

Over the last several years, semantic image segmentation based on deep neural networks has been greatly advanced. On the other hand, single-instance segmentation still remains a challenging problem. In this paper, we introduce a framework for segmenting instances of a common object class by multiple active contour evolution over semantic segmentation maps of images obtained through fully convolutional

This paper investigates automatic digitization with complete coverage of large and complex environments using a TLS or a mobile scanner. We propose an adaptive multi-objective view-planner that can operate in an unknown environment to provide guidance for the human operator and ease the scanning task or by a mobile robot for an automatic exploration of the environment. The proposed view-planner is

The analysis of topographic time series is often based on bitemporal change detection and quantification. For 3D point clouds, acquired using laser scanning or photogrammetry, random and systematic noise has to be separated from the signal of surface change by determining the minimum detectable change. To analyse geomorphic change in point cloud data, the multiscale model-to-model cloud comparison

To improve the accuracy of wood and leaf points classification for individual tree, this paper proposed a separation method based on mode points evolution from terrestrial LiDAR point clouds. In the proposed method, the Mean Shift method was used to first acquire the mode points, which were then adopted as nodes to build a network graph for the individual tree. By path retracing and calculating the

Urban land-use maps outlining the distribution, pattern, and composition of various land use types are critically important for urban planning, environmental management, disaster control, health protection, and biodiversity conservation. Recent advances in remote sensing and social sensing data and methods have shown great potentials in mapping urban land use categories, but they are still constrained

Image line segment detection is a fundamental problem in computer vision and remote sensing. Although numerous state-of-the-art methods have shown great performance for straight line segment detection, line segment detection for distorted images without undistortion is still a challenging problem. Besides, there is a lack of a unified line segment detection framework for both distorted and undistorted

In this paper, we propose a novel approach that reconstructs the indoor scene in a structure-aware manner and produces two meshes with different levels of abstraction. To be precise, we start from the raw triangular mesh of indoor scene and decompose it into two parts: structure and non-structure objects. On the one hand, structure objects are defined as significant permanent parts in the indoor environment

With the increasing demand of autonomous systems, pixelwise semantic segmentation for visual scene understanding needs to be not only accurate but also efficient for potential real-time applications. In this paper, we propose Context Aggregation Network, a dual branch convolutional neural network, with significantly lower computational costs as compared to the state-of-the-art, while maintaining a