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Found 10 papers, 3 papers with code
Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data
Large Vision-Language Models (VLMs) have demonstrated impressive performance on complex tasks involving visual input with natural language instructions.
Taking it further: leveraging pseudo labels for field delineation across label-scarce smallholder regions
We then used the human-annotated labels and the pseudo labels for model fine-tuning and compared predictions against human field annotations (n = 2, 199).
Combining Deep Learning and Street View Imagery to Map Smallholder Crop Types
To date, however, crop type maps remain challenging to create in low and middle-income countries due to a lack of ground truth labels for training machine learning models.
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.
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.
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.
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.
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.
