相关论文: Sims: An Interactive Tool for Geospatial Matching …
Satellite-based slum segmentation holds significant promise in generating global estimates of urban poverty. However, the morphological heterogeneity of informal settlements presents a major challenge, hindering the ability of models…
The wealth of data being gathered about humans and their surroundings drives new machine learning applications in various fields. Consequently, more and more often, classifiers are trained using not only numerical data but also complex data…
Understanding the earth's climate system and how it might be changing is a preeminent scientific challenge. Global climate models are used to simulate past, present, and future climates, and experiments are executed continuously on an array…
We introduce an open-source system called SIGMA (short for "Situated Interactive Guidance, Monitoring, and Assistance") as a platform for conducting research on task-assistive agents in mixed-reality scenarios. The system leverages the…
Commercially available scheduling tools such as Primavera and Microsoft Project fail to provide information pertaining to the spatial aspects of construction project. A methodology using geographical information systems (GIS) is developed…
Organizations have realized the importance of data analysis and its benefits. This in combination with Machine Learning algorithms has allowed to solve problems more easily, making these processes less time-consuming. Neural networks are…
Recent advances in multimodal large language models(MLLMs) have led to remarkable progress in visual grounding, enabling fine-grained cross-modal alignment between textual queries and image regions. However, transferring such capabilities…
Data is arguably the most valuable asset of the modern world. In this era, the success of any data-intensive solution relies on the quality of data that drives it. Among vast amount of data that are captured, managed, and analyzed everyday,…
OpenStreetMap (OSM) is a community-based, freely available, editable map service that was created as an alternative to authoritative ones. Given that it is edited mainly by volunteers with different mapping skills, the completeness and…
Since its introduction, landscape genomics has developed quickly with the increasing availability of both molecular and topo-climatic data. The current challenges of the field mainly involve processing large numbers of models and…
Benchmarking Simultaneous Localization and Mapping (SLAM) algorithms is important to scientists and users of robotic systems alike. But through their many configuration options in hardware and software, SLAM systems feature a vast parameter…
In the big data era, massive amount of multimedia data with geo-tags has been generated and collected by mobile smart devices equipped with mobile communications module and position sensor module. This trend has put forward higher request…
Cloud Computing (CC) is a model for enabling on-demand access to a shared pool of configurable computing resources. Testing and evaluating the performance of the cloud environment for allocating, provisioning, scheduling, and data…
Evaluating the performance of Simultaneous Localization and Mapping (SLAM) algorithms is essential for scientists and users of robotic systems alike. But there are a multitude of different permutations of possible options of hardware setups…
Powered by the emerging large language models (LLMs), autonomous geographic information systems (GIS) agents have the potential to accomplish spatial analyses and cartographic tasks. However, a research gap exists to support fully…
Aggregated relative search frequencies offer a unique composite signal reflecting people's habits, concerns, interests, intents, and general information needs, which are not found in other readily available datasets. Temporal search trends…
Spatial-temporal forecasting plays an important role in many real-world applications, such as traffic forecasting, air pollutant forecasting, crowd-flow forecasting, and so on. State-of-the-art spatial-temporal forecasting models take…
Machine learning requires defining one's target variable for predictions or decisions, a process that can have profound implications for fairness, since biases are often encoded in target variable definition itself, before any data…
When humans play geolocation games such as GeoGuessr, they rely on concrete visual cues, such as road markings, vegetation, or architectural details, to infer where an image was captured. Whether image geolocation models rely on similar…
High-performance scientific applications require more and more compute power. The concurrent use of multiple distributed compute resources is vital for making scientific progress. The resulting distributed system, a so-called Jungle…