Mobile Spatial Data
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Efficient and Privacy-Preserving Distribution Statistics Analytics on Mobile Spatial Data
arXiv:2605.25791v2 Announce Type: replace Abstract: With the rapid development of mobile computing technology, massive amounts of spatial data are continuously generated from various mobile terminals and sensing devices, such as smartphones, connected vehicles, and drones. Performing efficient distributed statistical analysis on this data is crucial for real-time mobile computing applications. However, the constrained and dynamic nature of mobile environments exacerbates the privacy...
Light-induced quantum friction of carbon nanotubes in water
Abstract Friction slows down moving objects at both macroscopic and microscopic scales1. At the electronic level, quantum friction describes direct transfer of momentum between a liquid and the electrons of a solid2. Owing to its microscopic nature, this phenomenon remains experimentally challenging to capture3.
GeoLibre 1.0
Cloud-native GIS platform A lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. GeoLibre is built with Tauri, React, TypeScript, MapLibre GL JS, DuckDB-WASM Spatial, and deck.gl.
Building user-driven climate adaptation products
Abstract Climate adaptation products have traditionally been developed using a supply-driven model reliant on available climate information, leading to usability gaps1,2,3,4. To better meet user needs, the climate services field has recognized a need to shift towards a demand-driven model emphasizing co-production, that is, user-driven, scientifically informed products created through shared knowledge practices1,2,3,4,5. However, co-production can be challenging, especially for researchers...
Mobility-Embedded POIs: Learning What A Place Is and How It Is Used from Human Movement
arXiv:2601.21149v3 Announce Type: replace Abstract: Recent progress in geospatial foundation models highlights the importance of learning general-purpose representations for real-world locations, particularly points-of-interest (POIs) where human activity concentrates. Existing approaches, however, focus primarily on place identity derived from static textual metadata, or learn representations tied to trajectory context, which capture movement regularities rather than how places are actually...
Deep learning four decades of human migration
Abstract Human migration is a fundamental driver of global demographic change, shaping population structure, labour markets and social policy across countries1,2,3. Although long-term migration patterns are often linked to economic development4, they can shift rapidly in response to shocks such as conflict, environmental crises and political change5. Despite its importance, migration remains difficult to measure consistently: existing data are sparse, concentrated in high-income settings and...
Disaster-induced behavioral change restructures social networks toward bonding ties
Announce Type: new Abstract: Population displacement following environmental shocks reshapes the spatial organization of social interactions, often fragmenting existing ties and weakening community cohesion. Although social capital is widely recognized as a key determinant of resilience, its dynamic restructuring after disruption remains poorly quantified. Here, we develop a spatially embedded, dynamic network framework that operationalizes social capital as a network of repeated encounter...
LaGuardia Airport AI hologram answers traveler questions
Airports can feel like a maze when you are rushing to a gate, hunting for baggage claim or trying to find a lounge before boarding. Now, LaGuardia Airport's Terminal B wants to make that all feel a little less stressful with a life-sized AI hologram named Bridget.Bridget can hold a real conversation with you. She can answer questions about gates, shops, baggage claim and VIP lounges.
Crop Recommendation and Agricultural Query Answering System Using Spatio-Temporal Graph Neural Networks and Hybrid Retrieval Augmentation
Announce Type: new Abstract: This paper presents a unified system designed to support precision agriculture by integrating advanced weather prediction, crop recommendation, and a question-answering tool for farmers. We propose two deep learning models -- a Transformer-based Graph Neural Network and a Spatio-Temporal Graph Convolutional Network (STGCN) -- to forecast weather conditions for the next 30 days using data from 1,359 locations in Nepal. The STGCN outperforms the Transformer-based...
Impact of RTK Augmentation and INS Integration on GNSS Positioning Accuracy and Continuity: A Benchmarking Study on Inland Waterways
arXiv:2606.06358v1 Announce Type: new Abstract: RTK augmentation andINS integration are widely used to improve GNSS positioning performance. However, on inland waterways, bridges and surrounding structures can degrade satellite visibility and correction availability, causing RTK augmentation loss, and GNSS/INS fusion transients.