Supraglacial lakes in the Arctic undergo seasonal and glacial-activity-induced changes, providing profound insights into ice dynamics and climate changes in these sensitive regions. However, the morphological complexity of these lakes, compounded by the ...

The ACM SIGSPATIAL Cup 2023 proposed the challenge to identify and map supraglacial lakes in Greenland in satellite imagery. The peculiarities of supraglacial lakes pose a hard problem for semantic segmentation and object detection tasks because the ...

Rising summer temperatures in Greenland have accelerated the formation of supraglacial lakes. Since these lakes play a significant role in ice sheet dynamics and bed lubrication, their continuous monitoring in a warming Arctic is becoming essential. The ...

Mining causal relationships within co-location patterns is a crucial aspect of knowledge discovery, with broad applications spanning across ecosystems, praxiology, and epidemiology. However, existing approaches to solving this problem still have ...

This paper introduces innovative approaches for extracting geographic objects from scanned map images. Overcoming the challenges associated with labor-intensive data labeling and inaccurate extraction results, we present two data labeling methods and an ...

Harmful Algal Blooms (HABs) present significant environmental and public health threats. Recent machine learning-based HABs monitoring methods often rely solely on unimodal data, e.g., satellite imagery, overlooking crucial environmental factors such as ...

Changes in place names offer insight into regions' culture, politics, and geographical characteristics. This paper proposes an automatic approach to generate time-sequenced maps that show place name changes on a large number of maps from different time ...

In the realm of transportation network companies (TNCs), the utilization of demand forecasting models and algorithms has revolutionized essential services, such as guiding idle vehicles, efficient carpooling, rate fluctuations, and price surging. In this ...

This study presents a novel approach to designing a system for disability-aware sidewalk routing that considers the specific needs and preferences of individuals with mobility impairments. The study involves creating a multi-objective personalized ...

The proliferation of motion sensors has significantly contributed to the availability of mobility data. An important line of research focuses on augmenting these datasets with diverse semantic information, referred to as aspects, thereby yielding ...

Capturing the universal movement pattern and simulating human mobility is one of the most important trajectory data-mining tasks. Most of the current mobility modeling methods are specially designed to solve a specific task, which leads to questions ...

Routing long trips of electric vehicles (EVs) is in growing demand and a non-trivial task as charging stops have to be planned along the way. Developing and testing realistic EV routing algorithms is challenging as multiple factors have to be considered ...

Locating undiscovered deposits of critical minerals requires accurate geological data. However, most of the 100,000 historical geological maps of the United States Geological Survey (USGS) are in raster format. This hinders critical mineral assessment. ...

Bavaria Buildings is a large, analysis-ready dataset providing openly available co-registered 40cm aerial imagery of Upper Bavaria paired with building footprint information. The Bavaria Buildings dataset (BBD) contains 18205 orthophotos of 2500 × 2500 ...

Accessing realistic human movements (aka trajectories) is essential for many application domains, such as urban planning, transportation, and public health. However, due to privacy and commercial concerns, real-world trajectories are not readily ...

Measuring spatial accessibility to healthcare resources and facilities has long been an important problem in public health. For example, during disease outbreaks, sharing spatial accessibility data such as individual travel distances to health facilities ...

Road safety is a major global public health concern, and effective prediction of traffic accidents at a fine-grained spatial scale plays a critical role in reducing roadway deaths and serious injuries. However, previous studies have either overlooked ...

Accurate traffic speed prediction is essential for road safety and effective traffic management. However, this task is challenging due to the complex spatial and temporal interactions. Current traffic speed prediction approaches typically focus on short-...

This paper presents a visionary perspective on developing Artificial General Intelligence (AGI) in the field of human mobility research. Human mobility profoundly influences our daily lives, impacting transportation systems, urban planning, logistics, ...

Chicago, IL has been undergoing significant demographic and racial shifts as a result of gentrification and displacement. To resolve the risk, it is necessary to monitor the socioeconomic characteristics of regions and detect regions with drastic ...