GIS

Geographical units densely connected by human movements can be treated as a geospatial community. Detecting geospatial communities in a mobility network reveals key characteristics of human movements and urban structures. Recent studies have found ...

Geographic phenomena are considered complex due to the heterogeneous nature of spatial dependencies. It is impossible to specify a universal law described in statistical or physical languages that can perfectly characterize a real-world geographic ...

Extracting text from historical maps using Optical Character Recognition (OCR) engines often results in partially or incorrectly recognized words due to complex map content. Previous work utilizes lexical-based approaches with linguistic context or ...

Recent years brought advancements in using neural networks for representation learning of various language or visual phenomena. New methods freed data scientists from hand-crafting features for common tasks. Similarly, problems that require considering ...

Traffic flow prediction, as one of the prominent tasks in intelligent transportation systems, is challenging due to underlying complex spatiotemporal characteristics. Consideration of historical spatial and temporal dependencies is essential for the ...

Visual Question Answering (VQA) on remote sensing imagery can help non-expert users in extracting information from Earth observation data. Current approaches follow a neural encoder-decoder design, combining convolutional and recurrent encoders together ...

This study investigated multi-modal learning as a stand-alone solution to image geo-localization problems. Based on the successful trials on the contrastive language-image pre-training (CLIP) model, we developed GEo-localization Multi-modal (GEM) models,...

This paper introduces a real-time GeoAI workflow for large-scale image analysis and the segmentation of Arctic permafrost features at a fine-granularity. Very high-resolution (0.5m) commercial imagery is used in this analysis. To achieve real-time ...

Density-based clustering methods are frequently used to define spatial clusters and outliers (noise) for location-only data. Different algorithms for solving this problem emerged over the past few decades, with their main difference being the numerical ...

Image registration that aligns multi-temporal or multi-source images is vital for tasks like change detection and image fusion. Thanks to the advance and large-scale practice of modern surveying methods, multi-temporal historical maps can be unlocked ...

Geotagged Social Media (GTSM) data, especially geotagged tweets are valuable sources of information for many important applications. Only small portions of geotagged tweets are available (less than 3%). Identifying tweet location is a challenging ...

Region embedding is a primary task for a wide variety of urban-related downstream applications. However, many existing embedding techniques neglected the fact that the regions in a city have been developed differently by many factors such as planning ...