activity
20202023
most citedMETER-ML: A Multi-Sensor Earth Observation Benchmark for Automated Methane Source Mapping

9 citations · 27 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2023★ 1 cited

Detecting Neighborhood Gentrification at Scale via Street-level Visual Data

Tianyuan Huang, Timothy Dai, Zhecheng Wang +5

Neighborhood gentrification plays a significant role in shaping the social and economic well-being of both individuals and communities at large. While some efforts have been made t…

cs.LG2022

Improving debris flow evacuation alerts in Taiwan using machine learning

Yi-Lin Tsai, Jeremy Irvin, Suhas Chundi +3

Taiwan has the highest susceptibility to and fatalities from debris flows worldwide. The existing debris flow warning system in Taiwan, which uses a time-weighted measure of rainfa…

cs.CV2022★ 9 cited

METER-ML: A Multi-Sensor Earth Observation Benchmark for Automated Methane Source Mapping

Bryan Zhu, Nicholas Lui, Jeremy Irvin +7

Reducing methane emissions is essential for mitigating global warming. To attribute methane emissions to their sources, a comprehensive dataset of methane source infrastructure is…

cs.LG2021★ 8 cited

Learning Neighborhood Representation from Multi-Modal Multi-Graph: Image, Text, Mobility Graph and Beyond

Tianyuan Huang, Zhecheng Wang, Hao Sheng +2

Recent urbanization has coincided with the enrichment of geotagged data, such as street view and point-of-interest (POI). Region embedding enhanced by the richer data modalities ha…

cs.CV2020★ 3 cited

Effective Data Fusion with Generalized Vegetation Index: Evidence from Land Cover Segmentation in Agriculture

Hao Sheng, Xiao Chen, Jingyi Su +2

How can we effectively leverage the domain knowledge from remote sensing to better segment agriculture land cover from satellite images? In this paper, we propose a novel, model-ag…

cs.CV2020★ 6 cited

The 1st Agriculture-Vision Challenge: Methods and Results

Mang Tik Chiu, Xingqian Xu, Kai Wang +39

The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially f…