Search Results for author:
Found 26 papers, 10 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.
HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing
Small farms contribute to a large share of the productive land in developing countries.
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.
With big data come big problems: pitfalls in measuring basis risk for crop index insurance
More precisely, we derive the asymptotic distribution of the relative share of the first eigenvalue of the covariance matrix, a measure of systematic risk in index insurance.
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.
Optimal index insurance and basis risk decomposition: an application to Kenya
Basis risk arises from two fundamental sources: the intrinsic heterogeneity within an insurance zone (zonal risk), and the lack of predictive accuracy of the index (design risk).
On the benefits of index insurance in US agriculture: a large-scale analysis using satellite data
Despite its theoretical appeal, demand for index insurance has remained low in many developing countries, triggering a debate on the causes of the low uptake.
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.
Meta-Learning for Few-Shot Land Cover Classification
This indicates that model optimization with meta-learning may benefit tasks in the Earth sciences whose data show a high degree of diversity from region to region, while traditional gradient-based supervised learning remains suitable in the absence of a feature or label shift.
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.
Tile2Vec: Unsupervised representation learning for spatially distributed data
Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and computer vision tasks.
Hierarchical Modeling of Seed Variety Yields and Decision Making for Future Planting Plans
Eradicating hunger and malnutrition is a key development goal of the 21st century.
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.
Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data
Agricultural monitoring, especially in developing countries, can help prevent famine and support humanitarian efforts.
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.
