Search Results for author:
Found 37 papers, 22 papers with code
FLAVARS: A Multimodal Foundational Language and Vision Alignment Model for Remote Sensing
Remote sensing imagery is dense with objects and contextual visual information.
Distribution Shifts at Scale: Out-of-distribution Detection in Earth Observation
The core novelty lies in generating surrogate labels by integrating information from ID data and unknown distributions, enabling OOD detection at scale.
Sims: An Interactive Tool for Geospatial Matching and Clustering
Acquiring, processing, and visualizing geospatial data requires significant computing resources, especially for large spatio-temporal domains.
Local vs. Global: Local Land-Use and Land-Cover Models Deliver Higher Quality Maps
To address this issue, we propose a data-centric framework with a teacher-student model setup, which uses diverse data sources of satellite images and label examples to produce local land-cover maps.
Fields of The World: A Machine Learning Benchmark Dataset For Global Agricultural Field Boundary Segmentation
Crop field boundaries are foundational datasets for agricultural monitoring and assessments but are expensive to collect manually.
Analyzing Decades-Long Environmental Changes in Namibia Using Archival Aerial Photography and Deep Learning
This study explores object detection in historical aerial photographs of Namibia to identify long-term environmental changes.
Bootstrapping Rare Object Detection in High-Resolution Satellite Imagery
Rare object detection is a fundamental task in applied geospatial machine learning, however is often challenging due to large amounts of high-resolution satellite or aerial imagery and few or no labeled positive samples to start with.
A Change Detection Reality Check
In recent years, there has been an explosion of proposed change detection deep learning architectures in the remote sensing literature.
Mission Critical -- Satellite Data is a Distinct Modality in Machine Learning
Satellite data has the potential to inspire a seismic shift for machine learning -- one in which we rethink existing practices designed for traditional data modalities.
Weak Labeling for Cropland Mapping in Africa
We conduct experiments to demonstrate the benefits of the improved weak labels generated by our method.
Seeing the roads through the trees: A benchmark for modeling spatial dependencies with aerial imagery
In this work we propose a road segmentation benchmark dataset, Chesapeake Roads Spatial Context (RSC), for evaluating the spatial long-range context understanding of geospatial machine learning models and show how commonly used semantic segmentation models can fail at this task.
Ranked #1 on
Road Segmentation
on ChesapeakeRSC
Open Datasheets: Machine-readable Documentation for Open Datasets and Responsible AI Assessments
This paper introduces a no-code, machine-readable documentation framework for open datasets, with a focus on responsible AI (RAI) considerations.
SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery
The resulting SatCLIP location encoder efficiently summarizes the characteristics of any given location for convenient use in downstream tasks.
Poverty rate prediction using multi-modal survey and earth observation data
This work presents an approach for combining household demographic and living standards survey questions with features derived from satellite imagery to predict the poverty rate of a region.
Rapid building damage assessment workflow: An implementation for the 2023 Rolling Fork, Mississippi tornado event
Rapid and accurate building damage assessments from high-resolution satellite imagery following a natural disaster is essential to inform and optimize first responder efforts.
SSL4EO-L: Datasets and Foundation Models for Landsat Imagery
The Landsat program is the longest-running Earth observation program in history, with 50+ years of data acquisition by 8 satellites.
Open Data on GitHub: Unlocking the Potential of AI
GitHub is the world's largest platform for collaborative software development, with over 100 million users.
Revisiting pre-trained remote sensing model benchmarks: resizing and normalization matters
Research in self-supervised learning (SSL) with natural images has progressed rapidly in recent years and is now increasingly being applied to and benchmarked with datasets containing remotely sensed imagery.
Mask Conditional Synthetic Satellite Imagery
In this paper we propose a mask-conditional synthetic image generation model for creating synthetic satellite imagery datasets.
Fast building segmentation from satellite imagery and few local labels
Innovations in computer vision algorithms for satellite image analysis can enable us to explore global challenges such as urbanization and land use change at the planetary level.
Resolving label uncertainty with implicit posterior models
We propose a method for jointly inferring labels across a collection of data samples, where each sample consists of an observation and a prior belief about the label.
An Artificial Intelligence Dataset for Solar Energy Locations in India
Using this dataset, we measure the solar footprint across India and quantified the degree of landcover modification associated with the development of PV infrastructure.
TorchGeo: Deep Learning With Geospatial Data
Deep learning methods are particularly promising for modeling many remote sensing tasks given the success of deep neural networks in similar computer vision tasks and the sheer volume of remotely sensed imagery available.
Resolving label uncertainty with implicit generative models
In prediction problems, coarse and imprecise sources of input can provide rich information about labels, but are not readily used by discriminative learners.
Detecting Cattle and Elk in the Wild from Space
Localizing and counting large ungulates -- hoofed mammals like cows and elk -- in very high-resolution satellite imagery is an important task for supporting ecological studies.
Temporal Cluster Matching for Change Detection of Structures from Satellite Imagery
For instance, in rural settings, the pre-construction area may look similar to the surrounding environment until the building is constructed.
Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary
We show that by using the FELICIA mechanism, a data owner with limited image samples can generate high-quality synthetic images with high utility while neither data owners has to provide access to its data.
High-resolution land cover change from low-resolution labels: Simple baselines for the 2021 IEEE GRSS Data Fusion Contest
We present simple algorithms for land cover change detection in the 2021 IEEE GRSS Data Fusion Contest.
Model Generalization in Deep Learning Applications for Land Cover Mapping
Recent work has shown that deep learning models can be used to classify land-use data from geospatial satellite imagery.
Mining self-similarity: Label super-resolution with epitomic representations
We show that simple patch-based models, such as epitomes, can have superior performance to the current state of the art in semantic segmentation and label super-resolution, which uses deep convolutional neural networks.
Local Context Normalization: Revisiting Local Normalization
In many vision applications the local spatial context of the features is important, but most common normalization schemes including Group Normalization (GN), Instance Normalization (IN), and Layer Normalization (LN) normalize over the entire spatial dimension of a feature.
Human-Machine Collaboration for Fast Land Cover Mapping
This bi-directional feedback loop allows humans to learn how the model responds to new data.
Large Scale High-Resolution Land Cover Mapping With Multi-Resolution Data
The land cover mapping problem, at country-level scales, is challenging for common deep learning methods due to the scarcity of high-resolution labels, as well as variation in geography and quality of input images.
Label super-resolution networks
We present a deep learning-based method for super-resolving coarse (low-resolution) labels assigned to groups of image pixels into pixel-level (high-resolution) labels, given the joint distribution between those low- and high-resolution labels.
Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations
Deep learning is increasingly used in decision-making tasks.
A Machine Learning Approach to Modeling Human Migration
Traditional human mobility models, such as gravity models or the more recent radiation model, predict human mobility flows based on population and distance features only.
A Deep Learning Approach for Population Estimation from Satellite Imagery
We validate these models in two ways: quantitatively, by comparing our model's grid cell estimates aggregated at a county-level to several US Census county-level population projections, and qualitatively, by directly interpreting the model's predictions in terms of the satellite image inputs.
