# Mapillary_Annotation Using the API of the crowdsourcing platform Mapillary, we automatically download all available street-level images over the area of interest in the Netherlands for the year of 2017. Then, each downloaded image is matched with the corresponding LPIS object(s) it illustrates. We annotate images that are taken either towards the windshield direction (Case 1) or the window direction (Case 2). We move the initial geo-location coordinates `(lat1, lon1)` to new coordinates `(lat2, lon2)` that are `d = 10m` away in the direction of angle θ. #### Distribution of Labels | Label | Count | | --- | --- | | `Grassland` | 40220 | | `Maize` | 4783 | | `Potatos` | 297 | | `Winter Wheat` | 127 | | `Sumer Barley` | 56 | | `Sugar Beet` | 36 | | `Rice` | 33 | | `Onions` | 29 | | **`Total`** | **45581** | link: https://github.com/Agri-Hub/Mapillary_Annotation # Space2Ground Dataset Space2Ground is a multi-level, multi-sensor, multi-modal dataset, annotated with grassland/non-grassland labels for agriculture monitoring. We combine Sentinel-1 SAR data, Sentinel-2 multispectral data and street-level images for the purpose of grassland detection. Only two classes: grassland or not link: https://github.com/Agri-Hub/Space2Ground # Paddy Rice Labeling Sites in South Korea (2018) The paddy rice was visually interpreted at 30 sites in South Korea. The sites were selected at each province by a proportional stratified sampling method according to the paddy rice area statistics (Statistics Korea), so the dataset can be used for the validation on model generalization over the entire country. The paddy rice areas were visually interpreted by using Google Earth Pro and street view services (https://map.naver.com, https://map.kakao.com) and updated to the state of 2018. link: https://zenodo.org/record/5846018#.Ys1CoHZBxmM # CropHarvest CropHarvest is an open source remote sensing dataset for agriculture with benchmarks. It collects data from a variety of agricultural land use datasets and remote sensing products. The dataset consists of 90,480 datapoints, of which 30,899 (34.2%) have multiclass labels. All other datapoints only have binary crop / non-crop labels. The datasets include 3 different types of labels: i) binary labels (crop/non crop) ii) FAO’s indicative crop classification labels, whcih resulted to 9 crop type groupings: cereals, vegetables and melons, fruits and nuts, oilseed crops, root/tuber crops, beverage and spice crops, leguminous crops, sugar crops, and other crops iii) crop-type labels, if available. https://github.com/nasaharvest/cropharvest/blob/main/diagrams/labels_spatial_distribution.png?raw=true | Source | Area of Focus | Number of labels used | Label Type | License | |-------------|---------------|----------------------:|------------|----------| |NASA Harvest | Rwanda | 3,600 | Binary | CC BY-4.0| |NASA Harvest | Kenya | 2,704 | Binary | CC BY-4.0| |NASA Harvest | Ethiopia | 830 | Binary | CC BY-4.0| |NASA Harvest | Sudan | 422 | Binary | CC BY-4.0| |NASA Harvest | Mali | 142 | Binary | CC BY-4.0| |NASA Harvest | Brazil | 36 | Binary | CC BY-4.0| |[[1]](#1) | Togo | 1,582 | Binary | CC BY-4.0| |[[2]](#2)| France (Ile de France) | 6,184 |Multi-class| etalab Open License | |[[2]](#2)| France (Reunion) | 2,776 |Multi-class| etalab Open License | |[[2]](#2)| France (Martinique) | 2,421 |Multi-class| etalab Open License | |[[3]](#3)| Global | 35,866 | Binary | CC BY-3.0| |[[4]](#4)| Global | 14,976 | Multi-class| LP DAAC | |[[5]](#5)| Canada | 9,088 | Multi-class| Open Government License | |[[6]](#6)| Uzbekistan, Tajikistan | 5,302 | Multi-class| CC BY-4.0| |[[7]](#7)| Germany | 2,550 | Multi-class| DL-DE->BY-2.0| |[[8]](#8)| Brazil | 800 | Multi-class| CC BY-4.0| |[[9]](#9)| Tanzania | 392 | Multi-class| CC BY-4.0| |[[10]](#10)| Kenya | 319 | Multi-class| CC BY-SA-4.0| |[[11]](#11)| Uganda | 233 | Multi-class| CC BY-4.0| | Harvest Partner | Mali | 148 | Multi-class| CC BY-4.0| | FEWS NET| Zimbabwe | 49 | Multi-class| CC BY-SA-4.0| link: https://github.com/nasaharvest/cropharvest # EuroCrops EuroCrops is a dataset collection combining all publicly available self-declared crop reporting datasets from countries of the European Union. The project is funded by the German Space Agency at DLR on behalf of the Federal Ministry for Economic Affairs and Climate Action (BMWK). This work is licensed under a Creative Commons Attribution 4.0 International License. Right now EuroCrops only includes vector data, but stay tuned for a version that includes satellite imagery! https://www.eurocrops.tum.de/images/f4.png link: https://github.com/maja601/EuroCrops # ZueriCrop The ZueriCrop dataset contains ground truth labels of 116,000 field instances. Each field instance consists of a polygon representing the borders of the field, and its dominant crop label in 2019. The ground truth labels of all 48 crop classes are provided by the Swiss Federal Office for Agriculture (FOAG) and correspond to the primary crop grown per field during the year. The input data is a time series of 71 multi-spectral Sentinel-2 Level-2A bottom-of-atmosphere reflectance images with a ground sampling distance (GSD) of 10 meters. All input images are atmospherically corrected using the Sen2Cor v2.8 software package. The dataset is collected over a 50 km × 48 km area in the Swiss Cantons of Zurich and Thurgau between January 2019 and December 2019. The entire scene is subdivided into smaller patches of 24 px×24 px. Patches without any ground-truth information are discarded. In the remaining patches the fraction of pixels without reference label is ≈48%. Only those four spectral channels available at the highest, 10 m resolution (Red, Green, Blue, and Near-Infrared) are used. link: https://github.com/0zgur0/ms-convSTAR # PASTIS PASTIS is a benchmark dataset for panoptic and semantic segmentation of agricultural parcels from satellite time series. It contains 2,433 patches within the French metropolitan territory with panoptic annotations (instance index + semantic label for each pixel). Each patch is a Sentinel-2 multispectral image time series of variable length. PASTIS dataset has been extended from the initial publication with aligned radar Sentinel-1 observations for all 2,433 patches in addition to the Sentinel-2 images. For each patch, approximately 70 observations of Sentinel-1 have been added in ascending orbit, and 70 observations in descending orbit. PASTIS-R can be used to evaluate optical-radar fusion methods for parcel-based classification, semantic segmentation, and panoptic segmentation. https://github.com/VSainteuf/utae-paps/raw/main/gfx/predictions.png link: https://github.com/VSainteuf/utae-paps # CV4A Kenya This dataset was produced as part of the Crop Type Detection competition at the Computer Vision for Agriculture (CV4A) Workshop at the ICLR 2020 conference. The ground reference data were collected by the PlantVillage team, and Radiant Earth Foundation curated the training dataset after inspecting and selecting more than 4,000 fields from the original ground reference data. The dataset has been split into training and test sets (3,286 in the train and 1,402 in the test). The dataset is cataloged in four tiles. These tiles are smaller than the original Sentinel-2 tile that has been clipped and chipped to the geographical area that labels have been collected. Each tile has a) 13 multi-band observations throughout the growing season. Each observation includes 12 bands from Sentinel-2 L2A product, and a cloud probability layer. The twelve bands are [B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B11, B12]. The cloud probability layer is a product of the Sentinel-2 atmospheric correction algorithm (Sen2Cor) and provides an estimated cloud probability (0-100%) per pixel. All of the bands are mapped to a common 10 m spatial resolution grid.; b) A raster layer indicating the crop ID for the fields in the training set; and c) A raster layer indicating field IDs for the fields (both training and test sets). Fields with a crop ID of 0 are the test fields. Crop ID Crop Type Number of fields in training data 1 Maize 1462 2 Cassava 829 3 Common Bean 98 4 Maize & Common Bean (intercropping) 487 5 Maize & Cassava (intercropping) 172 6 Maize & Soybean (intercropping) 160 7 Cassava & Common Bean (intercropping) 78 Link: https://github.com/sentinel-hub/cv4a-iclr-2020-starter-notebooks # TimeSen2Crop A pixel based dataset made up of more than 1 million samples of Sentinel 2 Time Series (TSs) associated to 16 crop types. This dataset includes atmospherically corrected images and reports the snow, shadows and clouds information per labeled unit. The provided TSs represent an agronomic year ranging from September 2017 to August 2018, using the publicly available Austrian crop type map based on farmer's declarations. TimeSen2Crop also includes a TS of Sentinel 2 images acquired in the following agronomic year (i.e., from September 2018 to August 2019). Link: https://drive.google.com/drive/folders/19YzUp0KhRnQN3IlU72vhJhCnzNJgFpsb # Sen4AgriNet The Sen4AgriNet dataset is built using Sentinel-2 images from different timestamps include all spectral bands that have different spatial resolution. On top of the dataset, it has been developed a series of functions such as spatio-temporal aggregations, to transform the original dataset according to the different AI problems. 5-year multitemporal Sentinel-2 patches Sentinel-1/2 data The initial version of Sen4AgriNet consists of approximately 225,000. Corregistered with open LPIS data for regions in Spain and France with a total size of 10TB 但是不知道有多少类别 link: https://www.sen4agrinet.space.noa.gr/ #