Top-down Green-ups: Satellite Sensing and Deep Models to Predict Buffelgrass Phenology
An invasive species of grass known as "buffelgrass" contributes to severe wildfires and biodiversity loss in the Southwest United States. We tackle the problem of predicting buffelgrass "green-ups" (i.e. readiness for herbicidal treatment). To make our predictions, we explore temporal, visual and multi-modal models that combine satellite sensing and deep learning. We find that all of our neural-based approaches improve over conventional buffelgrass green-up models, and discuss how neural model deployment promises significant resource savings.
Code (1)
Similar Papers 제목 키워드 기반
DeepMask: an algorithm for cloud and cloud shadow detection in optical satellite remote sensing images using deep residual network
Detecting and masking cloud and cloud shadow from satellite remote sensing images is a pervasive problem in the remote sensing community. Accurate and efficient detection of cloud and cloud shadow is an essential step to…
Cloud DetectionShadow DetectionUncertainty assessment in satellite-based greenhouse gas emissions estimates using emulated atmospheric transport
Monitoring greenhouse gas emissions and evaluating national inventories require efficient, scalable, and reliable inference methods. Top-down approaches, combined with recent advances in satellite observations, provide n…
Graph Neural NetworkSmall Spacecraft for Global Greenhouse Gas Monitoring
This work is devoted to the capabilities analysis of constellation and small spacecraft developed using CubeSat technology to solve promising problems of the Earth remote sensing in the area of greenhouse gases emissions…
Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space
Air pollution is a major driver of climate change. Anthropogenic emissions from the burning of fossil fuels for transportation and power generation emit large amounts of problematic air pollutants, including Greenhouse G…
TorchGeo: Deep Learning With Geospatial Data
Remotely sensed geospatial data are critical for applications including precision agriculture, urban planning, disaster monitoring and response, and climate change research, among others. Deep learning methods are partic…
Deep LearningTransfer Learning