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Found 15 papers, 7 papers with code
Application-Driven Innovation in Machine Learning
As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly important.
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.
Automatic characterization of boulders on planetary surfaces from high-resolution satellite images
Our neural network, BoulderNet, was trained from a dataset of > 33, 000 boulders in > 750 image tiles from Earth, the Moon, and Mars.
ConeQuest: A Benchmark for Cone Segmentation on Mars
Identifying pitted cones globally on Mars would be of great importance, but expert geologists are unable to sort through the massive orbital image archives to identify all examples.
Sat2Cap: Mapping Fine-Grained Textual Descriptions from Satellite Images
For a given location and overhead image, our model predicts the expected CLIP embeddings of the ground-level scenery.
Reflections from the Workshop on AI-Assisted Decision Making for Conservation
In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-21, 2022.
How accurate are existing land cover maps for agriculture in Sub-Saharan Africa?
Satellite Earth observations (EO) can provide affordable and timely information for assessing crop conditions and food production.
GEO-Bench: Toward Foundation Models for Earth Monitoring
Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to downstream tasks.
Lightweight, Pre-trained Transformers for Remote Sensing Timeseries
Machine learning methods for satellite data have a range of societally relevant applications, but labels used to train models can be difficult or impossible to acquire.
Ranked #1 on
Crop Classification
on CropHarvest - Kenya
TIML: Task-Informed Meta-Learning for Agriculture
When developing algorithms for data-sparse regions, a natural approach is to use transfer learning from data-rich regions.
Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark
Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to impressive increases in generalisation for downstream tasks.
Using transfer learning to study burned area dynamics: A case study of refugee settlements in West Nile, Northern Uganda
By comparing the district-level BA dynamic with the wider West Nile region, we aim to add understanding of the land management impacts of refugee settlements on their surrounding environments.
Resilient In-Season Crop Type Classification in Multispectral Satellite Observations using Growth Stage Normalization
Crop type classification using satellite observations is an important tool for providing insights about planted area and enabling estimates of crop condition and yield, especially within the growing season when uncertainties around these quantities are highest.
Rapid Response Crop Maps in Data Sparse Regions
A major challenge for developing crop maps is that many regions do not have readily accessible ground truth data on croplands necessary for training and validating predictive models, and field campaigns are not feasible for collecting labels for rapid response.
Field-Level Crop Type Classification with k Nearest Neighbors: A Baseline for a New Kenya Smallholder Dataset
Accurate crop type maps provide critical information for ensuring food security, yet there has been limited research on crop type classification for smallholder agriculture, particularly in sub-Saharan Africa where risk of food insecurity is highest.
