Corpus ID: 236447582

CalCROP21: A Georeferenced multi-spectral dataset of Satellite Imagery and Crop Labels

  title={CalCROP21: A Georeferenced multi-spectral dataset of Satellite Imagery and Crop Labels},
  author={Rahul Ghosh and Praveen Ravirathinam and Xiaowei Jia and Ankush Khandelwal and David J. Mulla and Vipin Kumar},
Mapping and monitoring crops is a key step towards sustainable intensification of agriculture and addressing global food security. A dataset like ImageNet that revolutionized computer vision applications can accelerate development of novel crop mapping techniques. Currently, the United States Department of Agriculture (USDA) annually releases the Cropland Data Layer (CDL) which contains crop labels at 30m resolution for the entire United States of America. While CDL is state of the art and isโ€ฆย Expand
Attention-augmented Spatio-Temporal Segmentation for Land Cover Mapping
A novel architecture is introduced that incorporates the UNet structure with Bidirectional LSTM and Attention mechanism to jointly exploit the spatial and temporal nature of satellite data and to better identify the unique temporal patterns of each land cover. Expand


SEN12MS - A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion
This work exploits the freely available data acquired by the Sentinel satellites of the Copernicus program implemented by the European Space Agency, as well as the cloud computing facilities of Google Earth Engine to provide a dataset consisting of 180,662 triplets of dual-pol synthetic aperture radar (SAR) image patches, multi-spectral Sentinel-2 image patch, and MODIS land cover maps. Expand
Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program
The National Agricultural Statistics Service (NASS) of the US Department of Agriculture (USDA) produces the Cropland Data Layer (CDL) product, which is a raster-formatted, geo-referenced,โ€ฆ Expand
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
A patch-based land use and land cover classification approach using Sentinel-2 satellite images that covers 13 spectral bands and is comprised of ten classes with a total of 27โ€‰000 labeled and geo-referenced images is presented. Expand
Accuracy, Bias, and Improvements in Mapping Crops and Cropland across the United States Using the USDA Cropland Data Layer
The overall findings demonstrate that the CDLs provide highly accurate annual measures of crops and cropland areas, and when used appropriately, are an indispensable tool for monitoring changes to agricultural landscapes. Expand
Does the U.S. Cropland Data Layer Provide an Accurate Benchmark for Landโ€Use Change Estimates?
Agronomy Journa l โ€ข Volume 108 , I s sue 1 โ€ข 2016 The U.S. CDL is an annual raster-based land-use map created by the USDA, National Agricultural Statistic Service (NASS). Th e CDL predicted land-usesโ€ฆ Expand
DeepSat: a learning framework for satellite imagery
This paper presents two new satellite datasets called SAT-4 and SAT-6, and proposes a classification framework that extracts features from an input image, normalizes them and feeds the normalized feature vectors to a Deep Belief Network for classification. Expand
DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images
  • Ilke Demir, K. Koperski, +6 authors R. Raskar
  • Computer Science
  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  • 2018
The DeepGlobe 2018 Satellite Image Understanding Challenge is presented, which includes three public competitions for segmentation, detection, and classification tasks on satellite images, and characteristics of each dataset are analyzed, and evaluation criteria for each task are defined. Expand
DOTA: A Large-Scale Dataset for Object Detection in Aerial Images
A large-scale Dataset for Object deTection in Aerial images (DOTA) is introduced and state-of-the-art object detection algorithms on DOTA are evaluated, demonstrating that DOTA well represents real Earth Vision applications and are quite challenging. Expand
Bigearthnet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding
Experimental results obtained in the framework of RS image scene classification problems show that a shallow Convolutional Neural Network architecture trained on the BigEarthNet provides much higher accuracy compared to a state-of-the-art CNN model pre-trained on the ImageNet. Expand
Deriving 2011 cultivated land cover data sets using usda National Agricultural Statistics Service historic Cropland Data Layers
  • C. Boryan, Z. Yang, L. Di
  • Computer Science, Mathematics
  • 2012 IEEE International Geoscience and Remote Sensing Symposium
  • 2012
It was found that accuracies were close among the cultivated data generated using the different models, and a comparison of the resulting cultivated data set accuracies to the accuracies of the original CDL input data was found. Expand