417 citations
- Digital Science (United States)US2 papers
- State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingCN2 papers
- University of Illinois Urbana-ChampaignUS2 papers
- Wuhan UniversityCN2 papers
- Amazon (United States)US1 paper
- American University in CairoEG1 paper
- Beijing University of Civil Engineering and ArchitectureCN1 paper
- Center For Remote Sensing (United States)US1 paper
- GCE Lab SchoolUS1 paper
- GDA (United States)US1 paper
- Graphic Science (United Kingdom)GB1 paper
- Louisiana State UniversityUS1 paper
7 papers
RegionGCN: Spatial-Heterogeneity-Aware Graph Convolutional Networks
Hao Guo, Han Wang, Di Zhu +3
Modeling spatial heterogeneity in the data generation process is essential for understanding and predicting geographical phenomena. Despite their prevalence in geospatial tasks, ne…
PRIME: A CyberGIS Platform for Resilience Inference Measurement and Enhancement
Debayan Mandal, Lei Zou, Rohan Singh Wilkho +6
In an era of increased climatic disasters, there is an urgent need to develop reliable frameworks and tools for evaluating and improving community resilience to climatic hazards at…
RBPGAN: Recurrent Back-Projection GAN for Video Super Resolution
Marwah Sulaiman, Zahraa Shehabeldin, Israa Fahmy +5
Recently, video super resolution (VSR) has become a very impactful task in the area of Computer Vision due to its various applications. In this paper, we propose Recurrent Back-Pro…
Quantum vacuum under mixed boundary conditions: the case for curved spacetime
Borzoo Nazari
Influence of gravity on the quantum vacuum of a massless minimally coupled scalar field under Robin boundary conditions on parallel plates is investigated. We introduce the detaile…
Geographically and temporally weighted neural networks for satellite-based mapping of ground-level PM2.5
Tongwen Li, Huanfeng Shen, Qiangqiang Yuan +1
The integration of satellite-derived aerosol optical depth (AOD) and station-measured PM2.5 provides a promising approach for obtaining spatial PM2.5 data. Several spatiotemporal m…
Estimating ground-level PM2.5 by fusing satellite and station observations: A geo-intelligent deep learning approach
Tongwen Li, Huanfeng Shen, Qiangqiang Yuan +2
Fusing satellite observations and station measurements to estimate ground-level PM2.5 is promising for monitoring PM2.5 pollution. A geo-intelligent approach, which incorporates ge…