Search Results for author:
Found 9 papers, 2 papers with code
A Support Vector Approach in Segmented Regression for Map-assisted Non-cooperative Source Localization
This paper proposes a segmented regression approach using 2D maps to estimate source location and propagation environment jointly.
Self-supervised Learning for Geospatial AI: A Survey
The proliferation of geospatial data in urban and territorial environments has significantly facilitated the development of geospatial artificial intelligence (GeoAI) across various urban applications.
Road Network Representation Learning with the Third Law of Geography
In response, we propose to endow road network representation with the principles of the recent Third Law of Geography.
LAMP: A Language Model on the Map
Large Language Models (LLMs) are poised to play an increasingly important role in our lives, providing assistance across a wide array of tasks.
Urban Region Embedding via Multi-View Contrastive Prediction
Recently, learning urban region representations utilizing multi-modal data (information views) has become increasingly popular, for deep understanding of the distributions of various socioeconomic features in cities.
SwG-former: A Sliding-Window Graph Convolutional Network for Simultaneous Spatial-Temporal Information Extraction in Sound Event Localization and Detection
Sound event localization and detection (SELD) involves sound event detection (SED) and direction of arrival (DoA) estimation tasks.
City Foundation Models for Learning General Purpose Representations from OpenStreetMap
This can be attributed to the intrinsic heterogeneity of geospatial data, which encompasses different data types, including points, segments and regions, as well as multiple information modalities, such as a spatial position, visual characteristics and textual annotations.
On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence
In this work, we explore the promises and challenges of developing multimodal foundation models for GeoAI.
Narrative Cartography with Knowledge Graphs
With the help of this tool, the retrieved data from KGs are directly materialized in a GIS format which is ready for spatial analysis and mapping.
