Understanding spatial relationships between objects in images is crucial for robotic navigation, augmented reality systems, and autonomous driving applications, among others. However, existing vision-language benchmarks often overlook explicit spatia...
Geospatial information is indispensable for various real-world applications and is thus a prominent part of today’s data science landscape. Geospatial data is primarily maintained and disseminated through spatial data infrastructures (SDIs). Ho...
There have been growing concerns around the world over the rising spatial inequality (SI) amid fast and vast globalization. This paper presents an effort to benchmark the conditions and trends of spatial inequality in 37 megaregions in the United Sta...
Large language models (LLMs) have demonstrated remarkable capabilities in document processing, data analysis, and code generation. However, the generation of spatial information in a structured and unified format remains a challenge, limiting their i...
Community detection can reveal unknown spatial structures embedded in spatial networks. Current spatial community detection methods are mostly modularity-based. However, due to the lack of appropriate spatial networks serving as a benchmark, the accu...
China’s rental housing market has just started to develop in recent years. It is relatively imperfect and lacks a clear reference for the pricing of rents, which is not fully transparent. A study on the rent formation mechanism of rental housin...
The global spread of green roofs is becoming increasingly important in the context of increasing urbanization and climate change as they provide multiple benefits, including urban heat island effect (UHI) mitigation, stormwater retention and urban fl...
Spatial transcriptomics combines gene expression data with spatial coordinates to allow for the discovery of detailed RNA localization, study development, investigating the tumor microenvironment, and creating a tissue atlas. A large range of spatial...
Technologies around the world produce and interact with geospatial data instantaneously, from mobile web applications to satellite imagery that is collected and processed across the globe daily. Big raster data allow researchers to integrate and unco...
Forestlands not only provide the conditions that support forested environments, but they also generate natural resources and ecosystem services that support human survival and social development. Using benchmark price to evaluate the economic value o...
Adversarial patch attacks threaten the reliability of modern vision models. We present PatchMap, the first spatially exhaustive benchmark of patch placement, built by evaluating over 1.5×108 forward passes on ImageNet validation images. PatchMa...
Through volunteering data, people can help assess information on various aspects of their surrounding environment. Particularly in natural resource management, Volunteered Geographic Information (VGI) is increasingly recognized as a significant resou...
In today’s competitive environment, multi-branch companies allocate their stores with the aim of expanding their territorial coverage to attract new customers and increase their market share. Consumer satisfaction surveys either produce global...
Regression algorithms are regularly used for improving the accuracy of satellite precipitation products. In this context, satellite precipitation and topography data are the predictor variables, and gauged-measured precipitation data are the dependen...
Progress in automatic sign language recognition and translation has been hindered by the scarcity of datasets available for the training of machine learning algorithms, a challenge that is even more acute for languages with smaller signing communitie...
Gridded satellite precipitation datasets are useful in hydrological applications as they cover large regions with high density. However, they are not accurate in the sense that they do not agree with ground-based measurements. An established means fo...
Grid structured visual data such as forms, tables, and game boards require models that pair pixel level perception with symbolic consistency under global constraints. Recent Pixel Language Models (PLMs) map images to token sequences with promising fl...
Hyperspectral image classification is widely applied in several fields. Since existing datasets focus on a single scene, current deep learning-based methods typically divide patches randomly on the same image as training and testing samples. This can...
Background: Discovering risk biomarkers in small benign breast disease (BBD) biopsies is constrained by scarce tissue and microanatomic heterogeneity of terminal duct lobular units (TDLUs). We tested whether tissue-sparing tissue microarray (TMA)&nda...
In eastern Canada, harvesting practices and spatial organization of harvested sites are modulated according to ecosystem forest management objectives. We determined how spatial organization affects efficiency by evaluating wood procurement costs. A c...
The analysis of the near-surface air temperature is vital for many applications such as urban heat islands and climate change studies. In particular, extreme weather events are typically localized and so should the corresponding adaptation measures....
This study proposes a geographically weighted (GW) quantile machine learning (GWQML) framework for soil moisture (SM) prediction, integrating spatial kernel functions with quantile-based prediction and uncertainty quantification. The framework incorp...
Energy system models that consider variable renewable energy sources (VRESs) are computationally complex. The greater spatial scope and level of detail entailed in the models exacerbates complexity. As a complexity-reduction approach, this paper cons...
Despite their importance, relatively little attention has been paid to vehicle routing problems with asymmetric costs (ACVRPs), or their benchmark instances. Taking advantage of recent advances in map application programming interfaces (APIs) and sha...
Vibration data from rotating machinery working in different conditions display different properties in spatial and temporal scales. As a result, insights into spatial- and temporal-scale structures of vibration data of rotating machinery are fundamen...
Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a standard tool used for absolute quantification of drugs in pharmacokinetic (PK) studies. However, all spatial information is lost during the extraction and elucidation of a drugs biodistr...
Accurate identification of spatial domains is essential in the analysis of spatial transcriptomics data in order to elucidate tissue microenvironments and biological functions. However, existing methods only perform domain segmentation based on local...
Region proposal network (RPN) based trackers employ the classification and regression block to generate the proposals, the proposal that contains the highest similarity score is formulated to be the groundtruth candidate of next frame. However, regio...
In this work, we develop and test proxy-based diagnostic tools for comparing freshwater ecosystem services (FWES) risks across an international array of freshwater ecosystems. FWES threats are increasing rapidly under pressure from population, climat...
As carbon intensity (CI) can better reflect the coordinated relationship between carbon emissions and economic growth, the related research has gradually increased in recent years. To better explore the influence of production technology and spatial...
On public benchmarks, current macro facial expression recognition technologies have achieved significant success. However, in real-life scenarios, individuals may attempt to conceal their true emotions. Conventional expression recognition often overl...
This study uses the spatial autoregressive model for panel data to empirically test the spatial peer effect of enterprises’ digital transformation by using a sample of Chinese listed companies during 2012–2021. We find that there is a sig...
Multi-object tracking in unmanned aerial vehicle (UAV) videos is a critical visual perception task with numerous applications. However, existing multi-object tracking methods, when directly applied to UAV scenarios, face significant challenges in mai...
This systematic review investigates recent advances and persistent challenges in Land Use and Land Cover (LULC) classification using Sentinel-2 imagery, emphasizing the gap between benchmark results and operational performance. Following PRISMA guide...
The proliferation of sophisticated malware poses a persistent threat to cybersecurity. While visualizing malware as images enables the use of Convolutional Neural Networks, standard architectures are often inefficient and struggle with the high spati...
Accurate traffic flow forecasting is pivotal for intelligent traffic control and guidance. Manually capturing the intricate dependencies between spatial and temporal dimensions in traffic data presents a significant challenge. Prior methods have prim...
Land surface temperature (LST) has a wide application in Earth Science-related fields, and spatial downscaling is an important method to retrieve high-resolution LST data. However, existing LST downscaling methods have difficulties in simultaneously...
Data augmentation with mixup has been proven effective in various machine learning tasks. However, previous methods primarily concentrate on generating previously unseen virtual examples using randomly selected mixed samples, which may overlook the i...
In transportation infrastructure systems, feature images and spatial characteristics are generally utilized as complementary elements derived from point clouds for road edge extraction, but the involvement of one or more hyperparameters in each makes...
Streak cameras, known for their ultra-high spatiotemporal resolution, rely heavily on the spatial resolution capabilities of their core component, the streak tube, to ensure engineering stability. However, factors such as assembly inaccuracies and ex...
In addressing the problem of indoor air pollution source localization, traditional methods have limitations such as strong sample dependence and low computational efficiency. This study uses a convolutional neural network to establish a pollution sou...
Access to schools is crucial in determining an area’s functioning and development, especially regarding housing development. This article presents an analysis of the spatial accessibility of educational services in the city. In Poland, municipa...
Theoretical and experimental studies on loadbearing masonry walls have shown the significant influence of the axial load level (i.e., precompression) and wall aspect ratio on in-plane lateral resistance. Nonetheless, the impact of the precompression...
Recent deep learning methods have achieved promising performance in infrared small target detection (IRSTD) but with high computational cost, limiting deployment or operation on resource-limited scenarios. There is an urgent need to develop both ligh...
GeoSPARQL is an important standard for the geospatial linked data community, given that it defines a vocabulary for representing geospatial data in RDF, defines an extension to SPARQL for processing geospatial data, and provides support for both qual...
At present, nonparametric subspace classifiers, such as collaborative representation-based classification (CRC) and sparse representation-based classification (SRC), are widely used in many pattern-classification and -recognition tasks. Meanwhile, th...
Single-object vision–language tracking has become an important research topic due to its potential in applications such as intelligent surveillance and autonomous driving. However, existing cross-modal alignment methods typically rely on contra...
The characterization and quantification of ecological interactions and the construction of species’ distributions and their associated ecological niches are of fundamental theoretical and practical importance. In this paper, we discuss a Bayesian inf...
24 February 2019
Improving the walkability of built environments to promote healthy lifestyles and reduce high body mass is increasingly considered in regional development plans. Walkability indexes have the potential to inform, benchmark and monitor these plans if t...
With the development of machine learning technology, research cases for spatial estimation through machine learning approach (MLA) in addition to the traditional geostatistical techniques are increasing. MLA has the advantage that spatial estimation...
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