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Found 10 papers, 2 papers with code
Annual field-scale maps of tall and short crops at the global scale using GEDI and Sentinel-2
In the current study, we leverage GEDI to develop wall-to-wall maps of short vs tall crops on a global scale at 10 m resolution for 2019-2021.
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.
Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning and weak supervision
Our best model uses 1. 5m resolution Airbus SPOT imagery as input, pre-trains a state-of-the-art neural network on France field boundaries, and fine-tunes on India labels to achieve a median Intersection over Union (IoU) of 0. 86 in India.
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.
Early- and in-season crop type mapping without current-year ground truth: generating labels from historical information via a topology-based approach
Land cover classification in remote sensing is often faced with the challenge of limited ground truth.
Combining GEDI and Sentinel-2 for wall-to-wall mapping of tall and short crops
High resolution crop type maps are an important tool for improving food security, and remote sensing is increasingly used to create such maps in regions that possess ground truth labels for model training.
Two Shifts for Crop Mapping: Leveraging Aggregate Crop Statistics to Improve Satellite-based Maps in New Regions
Still, in many regions crop type mapping with satellite data remains constrained by a scarcity of field-level crop labels for training supervised classification models.
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.
The Historical Impact of Anthropogenic Climate Change on Global Agricultural Productivity
Agricultural research has fostered productivity growth, but the historical influence of anthropogenic climate change on that growth has not been quantified.
