Zobrazeno 1 - 10
of 482
pro vyhledávání: '"Burke Marshall"'
Large Language Models (LLMs) inherently carry the biases contained in their training corpora, which can lead to the perpetuation of societal harm. As the impact of these foundation models grows, understanding and evaluating their biases becomes cruci
Externí odkaz:
http://arxiv.org/abs/2402.02680
Autor:
Khanna, Samar, Liu, Patrick, Zhou, Linqi, Meng, Chenlin, Rombach, Robin, Burke, Marshall, Lobell, David, Ermon, Stefano
Diffusion models have achieved state-of-the-art results on many modalities including images, speech, and video. However, existing models are not tailored to support remote sensing data, which is widely used in important applications including environ
Externí odkaz:
http://arxiv.org/abs/2312.03606
The application of machine learning (ML) in a range of geospatial tasks is increasingly common but often relies on globally available covariates such as satellite imagery that can either be expensive or lack predictive power. Here we explore the ques
Externí odkaz:
http://arxiv.org/abs/2310.06213
Autor:
Liu, Enci, Meng, Chenlin, Kolodner, Matthew, Sung, Eun Jee, Chen, Sihang, Burke, Marshall, Lobell, David, Ermon, Stefano
Building coverage statistics provide crucial insights into the urbanization, infrastructure, and poverty level of a region, facilitating efforts towards alleviating poverty, building sustainable cities, and allocating infrastructure investments and p
Externí odkaz:
http://arxiv.org/abs/2301.01449
Autor:
Walker, Kendra, Moscona, Ben, Jack, Kelsey, Jayachandran, Seema, Kala, Namrata, Pande, Rohini, Xue, Jiani, Burke, Marshall
Crop residue burning is a major source of air pollution in many parts of the world, notably South Asia. Policymakers, practitioners and researchers have invested in both measuring impacts and developing interventions to reduce burning. However, measu
Externí odkaz:
http://arxiv.org/abs/2209.10148
Autor:
Cong, Yezhen, Khanna, Samar, Meng, Chenlin, Liu, Patrick, Rozi, Erik, He, Yutong, Burke, Marshall, Lobell, David B., Ermon, Stefano
Unsupervised pre-training methods for large vision models have shown to enhance performance on downstream supervised tasks. Developing similar techniques for satellite imagery presents significant opportunities as unlabelled data is plentiful and the
Externí odkaz:
http://arxiv.org/abs/2207.08051
Automated tracking of urban development in areas where construction information is not available became possible with recent advancements in machine learning and remote sensing. Unfortunately, these solutions perform best on high-resolution imagery,
Externí odkaz:
http://arxiv.org/abs/2204.01736
IS-COUNT: Large-scale Object Counting from Satellite Images with Covariate-based Importance Sampling
Autor:
Meng, Chenlin, Liu, Enci, Neiswanger, Willie, Song, Jiaming, Burke, Marshall, Lobell, David, Ermon, Stefano
Object detection in high-resolution satellite imagery is emerging as a scalable alternative to on-the-ground survey data collection in many environmental and socioeconomic monitoring applications. However, performing object detection over large geogr
Externí odkaz:
http://arxiv.org/abs/2112.09126
Autor:
Yeh, Christopher, Meng, Chenlin, Wang, Sherrie, Driscoll, Anne, Rozi, Erik, Liu, Patrick, Lee, Jihyeon, Burke, Marshall, Lobell, David B., Ermon, Stefano
Progress toward the United Nations Sustainable Development Goals (SDGs) has been hindered by a lack of data on key environmental and socioeconomic indicators, which historically have come from ground surveys with sparse temporal and spatial coverage.
Externí odkaz:
http://arxiv.org/abs/2111.04724
Autor:
Zheng, Zhuo, Zhong, Yanfei, Zhang, Liangpei, Burke, Marshall, Lobell, David B., Ermon, Stefano
Publikováno v:
In Remote Sensing of Environment 15 December 2024 315