Search Results for author:
Found 25 papers, 9 papers with code
Large Language Models are Geographically Biased
Initially, we demonstrate that LLMs are capable of making accurate zero-shot geospatial predictions in the form of ratings that show strong monotonic correlation with ground truth (Spearman's $\rho$ of up to 0. 89).
DiffusionSat: A Generative Foundation Model for Satellite Imagery
Our method outperforms previous state-of-the-art methods for satellite image generation and is the first large-scale $\textit{generative}$ foundation model for satellite imagery.
GeoLLM: Extracting Geospatial Knowledge from Large Language Models
With GeoLLM, we observe that GPT-3. 5 outperforms Llama 2 and RoBERTa by 19% and 51% respectively, suggesting that the performance of our method scales well with the size of the model and its pretraining dataset.
Building Coverage Estimation with Low-resolution Remote Sensing Imagery
In this paper, we propose a method for estimating building coverage using only publicly available low-resolution satellite imagery that is more frequently updated.
Detecting Crop Burning in India using Satellite Data
Based on insights from this process, we discuss technical challenges of detecting crop residue burning from satellite imagery as well as challenges to measuring impacts, both of burning and of policy interventions.
SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery
Unsupervised pre-training methods for large vision models have shown to enhance performance on downstream supervised tasks.
Tracking Urbanization in Developing Regions with Remote Sensing Spatial-Temporal Super-Resolution
Automated tracking of urban development in areas where construction information is not available became possible with recent advancements in machine learning and remote sensing.
IS-COUNT: Large-scale Object Counting from Satellite Images with Covariate-based Importance Sampling
We show empirically that the proposed framework achieves strong performance on estimating the number of buildings in the United States and Africa, cars in Kenya, brick kilns in Bangladesh, and swimming pools in the U. S., while requiring as few as 0. 01% of satellite images compared to an exhaustive approach.
SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning
Our goals for SustainBench are to (1) lower the barriers to entry for the machine learning community to contribute to measuring and achieving the SDGs; (2) provide standard benchmarks for evaluating machine learning models on tasks across a variety of SDGs; and (3) encourage the development of novel machine learning methods where improved model performance facilitates progress towards the SDGs.
Using Satellite Imagery and Machine Learning to Estimate the Livelihood Impact of Electricity Access
In many regions of the world, sparse data on key economic outcomes inhibits the development, targeting, and evaluation of public policy.
Wildfire smoke plume segmentation using geostationary satellite imagery
Wildfires have increased in frequency and severity over the past two decades, especially in the Western United States.
Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis
High-resolution satellite imagery has proven useful for a broad range of tasks, including measurement of global human population, local economic livelihoods, and biodiversity, among many others.
Geography-Aware Self-Supervised Learning
Contrastive learning methods have significantly narrowed the gap between supervised and unsupervised learning on computer vision tasks.
Ranked #5 on
Semantic Segmentation
on SpaceNet 1
(using extra training data)
Predicting Livelihood Indicators from Community-Generated Street-Level Imagery
Major decisions from governments and other large organizations rely on measurements of the populace's well-being, but making such measurements at a broad scale is expensive and thus infrequent in much of the developing world.
Efficient Poverty Mapping using Deep Reinforcement Learning
The combination of high-resolution satellite imagery and machine learning have proven useful in many sustainability-related tasks, including poverty prediction, infrastructure measurement, and forest monitoring.
Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks
Using satellite imaging can be a scalable and cost effective manner to perform the task of farm parcel delineation to collect this valuable data.
Generating Interpretable Poverty Maps using Object Detection in Satellite Images
Accurate local-level poverty measurement is an essential task for governments and humanitarian organizations to track the progress towards improving livelihoods and distribute scarce resources.
Learning to Interpret Satellite Images in Global Scale Using Wikipedia
Despite recent progress in computer vision, finegrained interpretation of satellite images remains challenging because of a lack of labeled training data.
Predicting Economic Development using Geolocated Wikipedia Articles
Progress on the UN Sustainable Development Goals (SDGs) is hampered by a persistent lack of data regarding key social, environmental, and economic indicators, particularly in developing countries.
Mapping Missing Population in Rural India: A Deep Learning Approach with Satellite Imagery
Millions of people worldwide are absent from their country's census.
Semi-Supervised Multitask Learning on Multispectral Satellite Images Using Wasserstein Generative Adversarial Networks (GANs) for Predicting Poverty
Obtaining reliable data describing local poverty metrics at a granularity that is informative to policy-makers requires expensive and logistically difficult surveys, particularly in the developing world.
Learning to Interpret Satellite Images Using Wikipedia
Despite recent progress in computer vision, fine-grained interpretation of satellite images remains challenging because of a lack of labeled training data.
Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning
Our best models predict infrastructure quality with AUROC scores of 0. 881 on Electricity, 0. 862 on Sewerage, 0. 739 on Piped Water, and 0. 786 on Roads using Landsat 8.
Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning
Obtaining detailed and reliable data about local economic livelihoods in developing countries is expensive, and data are consequently scarce.
Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping
We train a fully convolutional CNN model to predict nighttime lights from daytime imagery, simultaneously learning features that are useful for poverty prediction.
