In this 2nd International Workshop on Searching and Mining Large Collections of Geospatial Data (GeoSearch 2023), we built on the success of the previous edition and continued to bring together the art of search engine construction with both geospatial data modeling, data processing, and management to provide a forum for researchers and practitioners interested in the general topic of GeoSearch.
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GESTALT: Geospatially Enhanced Search with Terrain Augmented Location Targeting
Geographic information systems (GIS) provide users with a means to efficiently search over spatial data given certain key pieces of information, like the coordinates or exact name of a location of interest. Current GIS capabilities do not enable users ...
A Critical Perceptual Pre-trained Model for Complex Trajectory Recovery
Trajectory on the road traffic is commonly collected at a low sampling rate, and trajectory recovery aims to recover a complete and continuous trajectory from the sparse and discrete inputs. Recently, sequential language models have been innovatively ...
Metric Indexing for the Earth Mover's Distance
The Earth Mover's Distance (EMD) has become a popular choice for applications in similarity search, particularly in applications such as few-shot image classification where it is observed to match human perceptions of image differences better than ...
Category based Fast Retrieval for Point-of-Interest Search
In the quest of achieving a seamless e-commerce and ride-hailing experience, the significance of effective Point-of-Interest (POI) search cannot be overstated. Yet, the relentless growth in number of POIs over the years, has given a rise to increased ...
COMPASS: Cardinal Orientation Manipulation and Pattern-Aware Spatial Search
The Spatial Pattern Matching paradigm offers a promising direction for searching with incomplete or imperfect information, but it is badly constrained by dependence on graph-based representations and computationally intensive search algorithms like ...
Is ChatGPT a game changer for geocoding - a benchmark for geocoding address parsing techniques
The remarkable success of GPT models across various tasks, including toponymy recognition motivates us to assess the performance of the GPT-3 model in the geocoding address parsing task. To ensure that the evaluation more accurately mirrors ...
Index Terms
- Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data