---
dataset_info:
- config_name: default
  features:
  - name: 1m
    sequence:
      sequence:
        sequence:
          sequence: uint8
  - name: chm
    sequence:
      sequence:
        sequence:
          sequence: uint8
  - name: rgb
    sequence:
      sequence:
        sequence:
          sequence: uint8
  - name: metadata
    struct:
    - name: bounds
      sequence: float64
    - name: epsg
      dtype: string
    - name: siteID
      dtype: string
    - name: timestamp
      sequence: string
  splits:
  - name: train
    num_bytes: 303349477315
    num_examples: 35501
  download_size: 240895951943
  dataset_size: 303349477315
- config_name: satellogic
  features:
  - name: 1m
    sequence:
      sequence:
        sequence:
          sequence: uint8
  - name: rgb
    sequence:
      sequence:
        sequence:
          sequence: uint8
  - name: metadata
    struct:
    - name: bounds
      sequence: float64
    - name: crs
      sequence: string
    - name: timestamp
      sequence: string
  splits:
  - name: train
    num_bytes: 3675346598134
    num_examples: 2967663
  download_size: 3528764568282
  dataset_size: 3675346598134
- config_name: sentinel_1
  features:
  - name: 10m
    sequence:
      sequence:
        sequence:
          sequence: uint8
  - name: metadata
    dtype: string
  splits:
  - name: train
    num_bytes: 1762041095796
    num_examples: 1049466
  download_size: 1487586838960
  dataset_size: 1762041095796
- config_name: sentinel_2
  features:
  - name: 10m
    sequence:
      sequence:
        sequence:
          sequence: uint8
  - name: 20m
    sequence:
      sequence:
        sequence:
          sequence: uint8
  - name: 40m
    sequence:
      sequence:
        sequence:
          sequence: uint8
  - name: rgb
    sequence:
      sequence:
        sequence:
          sequence: uint8
  - name: scl
    sequence:
      sequence:
        sequence:
          sequence: uint8
  - name: metadata
    struct:
    - name: s3Path
      sequence: string
    - name: solarAngles
      sequence: string
    - name: tileGeometry
      sequence: string
    - name: timestamp
      sequence: string
    - name: viewIncidenceAngles
      sequence: string
  splits:
  - name: train
    num_bytes: 14967110614854
  download_size: 12926621389874
  dataset_size: 14967110614854
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
- config_name: satellogic
  data_files:
  - split: train
    path: satellogic/**/train-*
- config_name: sentinel_1
  data_files:
  - split: train
    path: sentinel_1/train-*
- config_name: sentinel_2
  data_files:
  - split: train
    path: sentinel_2/**/train-*
license: cc-by-4.0
---
<style>
.prose :where(pre):not(:where([class~=not-prose] *)) {
    backgroun-color: "#e0e0e0";
</style>
# EarthView dataset

<!-- Dataset thumbnail -->
![EarthView](<dataset samples op1.gif>)

## Overview
The **EarthView** Dataset is a comprehensive collection of multispectral earth imagery. The dataset is divided into four distinct subsets sourced from Satellogic, Sentinel-1, Sentinel-2, and NEON imagers, each providing unique data.

The dataset is also available in [AWS Open Data registry](https://satellogic-earthview.s3.us-west-2.amazonaws.com/index.html).
And you can play and [navigate Satellogic's dataset in this Colab notebook. ![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.sandbox.google.com/github/satellogic/satellogic-earthview/blob/main/satellogic_earthview_exploration.ipynb)

## Dataset Viewer
Check the [EarthView Dataset Viewer](https://huggingface.co/spaces/satellogic/EarthView-Viewer) and [it's code](https://huggingface.co/spaces/satellogic/EarthView-Viewer/tree/main) for examples on how to access the images and navigate the dataset.

[![ ](viewer.png 'EarthView Dataset Viewer')](https://satellogic-earthview-viewer.hf.space/)

- [EarthView dataset](#earthview-dataset)
  - [Overview](#overview)
  - [Dataset Viewer](#dataset-viewer)
  - [Data Sources](#data-sources)
  - [Available Subsets](#available-subsets)
  - [Data Format](#data-format)
    - [Satellogic](#satellogic)
      - [Metadata](#metadata)
      - [Images](#images)
      - [Example (Jupyter Notebook)](#example-jupyter-notebook)
      - [Example (iterate)](#example-iterate)
    - [Sentinel-1](#sentinel-1)
      - [Metadata](#metadata-1)
      - [Images](#images-1)
      - [Example (Jupyter Notebook)](#example-jupyter-notebook-1)
    - [Neon](#neon)
      - [Metadata](#metadata-2)
      - [Images](#images-2)
      - [Example (Jupyter Notebook)](#example-jupyter-notebook-2)
    - [Sentinel-2](#sentinel-2)
      - [Metadata](#metadata-3)
      - [Images](#images-3)
      - [Example (Jupyter Notebook)](#example-jupyter-notebook-3) 
  - [Known Issues](#known-issues)
  - [Citation](#citation) 

## Data Sources
Each subset (AKA configuration) in the EarthView dataset includes samples representing specific patches of the Earth. Each source (satellite type) has different characteristics, so the details for the samples in each of the subsets are subtly different. We provide a very simple library to access the images in the subsets.

## Available Subsets

| Name | Samples | Unique locations | Products | Image Resolution |
|--|--|--|--|--|
| Satellogic   | ~6 million   | ~3 million   | RGB  | 1m |
|              |             |             | NIR (Near Infrared) | 1m
| Neon         | ~1 million   | ~0.3 million | RGB | 0.1m |
|              |             |             | Canopy Height Model (Lidar)| 1m
|              |             |             | Hyperspectral (369 bands) | 1m
| Sentinel-1   | ~5.2 million | ~1 million   | SAR (mapped to RGB) | 10m (from 20m)
| Sentinel-2   | ~10 million  | ~1 million   | RGB | 10m
| | | | NIR  | 10m
| | | | NIR / Red Edge / SWIR | 20m
| | | | Scene Classification Layer | 20m
| | | | Coastal-Aerosol / Water Vapour / Cirrus | 40 (from 60m)

## Data Format
Each subset has some peculiarities and a specific data format in the dataset. Each item (sample) in the dataset is a dictionary with a `metadata` field and one or more entries for the different image products available, such as `rgb`, `chm`, `1m`, `10m` (see below). All of the image fields are 4D arrays where dimensions are REVISITS, BANDS, HEIGHT, WIDTH.

We encourage you to use the supplied [`earthview` library](https://huggingface.co/spaces/satellogic/EarthView-Viewer/blob/main/earthview.py) to simplify accessing the dataset, metadata and images. (For now, download the single python file and place in the same directory as your scripts, or in Python's path)

Bellow you'll find details for each subset

### Satellogic
#### Metadata
  | key | description |
  |--|--|
  | `bounds` | [x_min, y_min, x_max, y_max] (bottom-left corner and top-right corner coordinates in (easting, northing) format, WGS 84 / UTM).
  | | `[178191.0, 8248444.0, 178575.0, 8248828.0]`
  | `crs` | EPSG code
  | | `['EPSG:32723']`
  | `timestamp` | list of timestamps corresponding to the capture dates (only date is valid)
  | | `['2022-07-21T00:00:00']`
  | | `['2022-07-21T00:00:00', '2022-07-25T00:00:00']`
  |`count` | Number of re-visits for the location (not present in the dataset, generated by `items_to_images()`)
  | | 2  

#### Images

Satellogic images are captured with Satellogic's MarkIV satellite fleet. The payload produces RGB and NIR images at 1m native resolution (no PAN sharpening). Each sample in the dataset has 1 or 2 re-visits per location.

  | key | Product | Image Resolution | Size (pixels) | Bands | Re-samples | Data type |
  |--|--|--|--|--|--|--|
  | rgb | RGB  | 1m | 384 x 384 | 3 | 1 or 2 | uint8 |
  | 1m  | NIR  | 1m | 384 x 384 | 1 | 1 or 2 | uint8 |

Note: Despite data type being stored in uint64, values range between 0 and 255 (uint8).

#### Example (Jupyter Notebook)
<table border="1" style="border-collapse: inherit;" width="100%" bgcolor="#f0f0f0"><tr><td>

```python
import numpy as np
import earthview as ev

data = ev.load_dataset("satellogic", shards=[10])  # shard is optional
sample = next(iter(data))

print(sample.keys())
print(np.array(sample['rgb']).shape)      # RGB Data
print(np.array(sample['1m']).shape)       # NIR Data
```
</td></tr></table>
<table border="1" style="border-collapse: inherit;" width="100%" bgcolor="#f0f0f0"><tr><td>

```
dict_keys(['1m', 'rgb', 'metadata'])
(1, 3, 384, 384)
(1, 1, 384, 384)
```
</td></tr></table>
<table border="1" style="border-collapse: inherit" width="100%" bgcolor="#f0f0f0"><tr><td>

```python
sample = ev.item_to_images("satellogic", sample)
display(sample)
display(*sample["rgb"])
display(*sample["1m"])
```
</td></tr></table>
<table border="1" style="border-collapse: inherit" width="100%" bgcolor="#f0f0f0"><tr><td>

```
{'1m': [<PIL.Image.Image image mode=L size=384x384>],
 'rgb': [<PIL.Image.Image image mode=RGB size=384x384>],
 'metadata': {'bounds': [[178191.0, 8248444.0, 178575.0, 8248828.0]],
  'crs': ['EPSG:32723'],
  'timestamp': ['2022-08-13T00:00:00']}}
```
</td></tr></table>

![Satellogic RGB](satellogic-rgb.png)

![Satellogic RGB](satellogic-nir.png)


#### Example (iterate)
</td></tr></table>
<table border="1" style="border-collapse: inherit" width="100%" bgcolor="#f0f0f0"><tr><td>

```python
from itertools import islice
import earthview as ev

data = ev.load_dataset("satellogic", shards=[10]) # shard is optional
datai = iter(data)
for sample in islice(datai, 10):
  sample = ev.item_to_images("satellogic", sample)
  print(sample["metadata"]["bounds"])
  sample["rgb"][0].show()

```
</td></tr></table>

### Sentinel-1
#### Metadata
  | key | description |
  |--|--|
  | `type` | `'Polygon'` Indicates the `coordinates` are the vertices of a Polygon
  | `coordinates` | Five coordinates of a closed Polygon.
  | | `[[[434520.0, 8715520.0], [438360.0, 8715520.0], [438360.0, 8711680.0], [434520.0, 8711680.0], [434520.0, 8715520.0]]]`
  | `crs` | EPSG code
  | | `'epsg:32736'`
  |`count` | Number of re-visits for the location (not present in the dataset, generated by `items_to_images()`)
  | | 6  

#### Images

  Sentinel-1 carries a Synthetic Aperture Radar (SAR) payload. The data (imagery) produced has two channels, for vertical and horizontal polarization. The data in the dataset contains just the two channels, the images returned by `item_to_images()` implements a [standard mapping](https://gis.stackexchange.com/questions/400726/creating-composite-rgb-images-from-sentinel-1-channels) to return RGB images. (see the example below). Samples in the dataset contain varied numbers of re-visits per location.

  | key | Product | Image Resolution | Size (pixels) | Bands | Re-samples | Data type |
  |--|--|--|--|--|--|--|
  | 10m | RGB  | 10m | 384 x 384 | 3 | 1 or more | uint8 |

Note: Despite data type being stored in uint64, values range between 0 and 255 (uint8).

#### Example (Jupyter Notebook)
<table border="1" style="border-collapse: inherit;" width="100%" bgcolor="#f0f0f0"><tr><td>

```python
import numpy as np
import earthview as ev

data = ev.load_dataset("sentinel_1", shards=[88])  # shard is optional

sample = next(iter(data))

print(sample.keys())
print(np.array(sample['rgb']).shape)      # RGB Data
print(np.array(sample['10m']).shape)       # NIR Data
```
</td></tr></table>
<table border="1" style="border-collapse: inherit;" width="100%" bgcolor="#f0f0f0"><tr><td>

```
dict_keys(['10m', 'metadata'])
(6, 2, 384, 384)
```
</td></tr></table>
<table border="1" style="border-collapse: inherit;" width="100%" bgcolor="#f0f0f0"><tr><td>

```python
sample = ev.item_to_images("sentinel_1", sample)
display(sample)
display(*sample["10m"][:2])
```
</td></tr></table>
<table border="1" style="border-collapse: inherit;" width="100%" bgcolor="#f0f0f0"><tr><td>

```
{'10m': [<PIL.Image.Image image mode=RGB size=384x384>,
  <PIL.Image.Image image mode=RGB size=384x384>,
  <PIL.Image.Image image mode=RGB size=384x384>,
  <PIL.Image.Image image mode=RGB size=384x384>,
  <PIL.Image.Image image mode=RGB size=384x384>,
  <PIL.Image.Image image mode=RGB size=384x384>],
 'metadata': {'type': 'Polygon',
  'crs': 'epsg:32736',
  'coordinates': [[[434520.0, 8715520.0],
    [438360.0, 8715520.0],
    [438360.0, 8711680.0],
    [434520.0, 8711680.0],
    [434520.0, 8715520.0]]]}}
```
</td></tr></table>

![Sentinel-1 RGB](sentinel_1-0.png)

![Sentinel-1 RGB](sentinel_1-1.png)

### Neon

In the dataset, the subset/configuration for NEON is called `default`, but when using the earthview library you should call it `neon`.

#### Metadata
  | key | description |
  |--|--|
  | `bounds` | [x_min, y_min, x_max, y_max] (bottom-left corner and top-right corner coordinates in (easting, northing). 
  | | `[-82.04138011662944,   29.634596313943526,   -82.04071312100113,   29.635179014437973]`
  | `epsg` | EPSG code
  | | `'EPSG:32617'` (this is an error, coordinates are actually `'ESPG:4326'`, the library (earthview) solves it)
  | `timestamp` | list of timestamps corresponding to the capture dates (only date is valid)
  | | `['2018-01-01T00:00:00', '2019-01-01T00:00:00', '2021-01-01T00:00:00']`
  | `siteID` | `'OSBS'` for Ordway-Swisher Biological Station
  |`count` | Number of re-visits for the location (not present in the dataset, generated by `items_to_images()`)
  | | 3

#### Images

The NEON subset is composed of very high resolution RGB images at 0.1m, 1m hyperspectral data (369 bands), and 1m Canopy Height Model out of a LIDAR sensor. Every sample in the dataset contains 3 re-visits.

  | key | Product | Image Resolution | Size (pixels) | Bands | Re-samples | Data type |
  |--|--|--|--|--|--|--|
  | rgb | RGB  | 0.1m | 640 x 640 | 3 | 3 | uint8 |
  | chm | Canopy Height Model  | 1m | 64 x 64 | 1 | 3 | uint8 |
  | 1m | Hyperspectral  | 1m | 64 x 64 | 369 | 3 | uint8 |

When using `item_to_images()` the Hyperspectral images are mapped, from the 369 bands to RGB using a meaningless mapping. Please, don't use it for anything else than an example.
Note that images were stored in uint64, although values range between 0 and 255 (uint8).

#### Example (Jupyter Notebook)
<table border="1" style="border-collapse: inherit;" width="100%" bgcolor="#f0f0f0"><tr><td>

```python
import numpy as np
import earthview as ev

data = ev.load_dataset("neon", shards=[100])  # shard is optional

sample = next(iter(data))

print(sample.keys())
print(np.array(sample['rgb']).shape)       # RGB Data
print(np.array(sample['chm']).shape)       # Canopy Height
print(np.array(sample['1m']).shape)        # Hyperspectral
```
</td></tr></table>
<table border="1" style="border-collapse: inherit;" width="100%" bgcolor="#f0f0f0"><tr><td>

```
dict_keys(['1m', 'chm', 'rgb', 'metadata'])
(3, 3, 640, 640)
(3, 1, 64, 64)
(3, 369, 64, 64)
```
</td></tr></table>
<table border="1" style="border-collapse: inherit;" width="100%" bgcolor="#f0f0f0"><tr><td>

```python
sample = ev.item_to_images("neon", sample)
display(sample)
display(sample["rgb"][0])
display(sample["1m"][0])
display(sample["chm"][0])
```
</td></tr></table>
<table border="1" style="border-collapse: inherit;" width="100%" bgcolor="#f0f0f0"><tr><td>

```
{'1m': [<PIL.Image.Image image mode=RGB size=64x64>,
  <PIL.Image.Image image mode=RGB size=64x64>,
  <PIL.Image.Image image mode=RGB size=64x64>],
 'chm': [<PIL.Image.Image image mode=L size=64x64>,
  <PIL.Image.Image image mode=L size=64x64>,
  <PIL.Image.Image image mode=L size=64x64>],
 'rgb': [<PIL.Image.Image image mode=RGB size=640x640>,
  <PIL.Image.Image image mode=RGB size=640x640>,
  <PIL.Image.Image image mode=RGB size=640x640>],
 'metadata': {'bounds': [-82.04138011662944,
   29.634596313943526,
   -82.04071312100113,
   29.635179014437973],
  'epsg': 'ESPG:4326',
  'siteID': 'OSBS',
  'timestamp': ['2018-01-01T00:00:00',
   '2019-01-01T00:00:00',
   '2021-01-01T00:00:00']}}
```
</td></tr></table>

![NEON RGB](neon-2-rgb.png)

![NEON Canopy Height](neon-2-chm.png)

![NEON Hyperspectral meaningless mapping](neon-2-1m.png)

### Sentinel 2

#### Metadata
  | key | description |
  |--|--|
  | `s3Path` | List of Sentinel 2 tiles. These are available in AWS Open Data Registry
  | | `['tiles/18/N/VF/2021/12/9/0',..., 'tiles/18/N/VF/2021/11/14/0']`
  | `solarAngles` | Solar angles in degrees
  | | `['{"azimuth": 140.394, "zenith": 30.3633}',..., '{"azimuth": 136.448, "zenith": 25.8697}']`  
  | `tileGeometry` | `'Polygon'` geometry
  | | `['{"type": "Polygon", "crs": "epsg:32618", "coordinates": [[[430680.0, 4020.0], [434520.0, 4020.0], [434520.0, 180.0], [430680.0, 180.0], [430680.0, 4020.0]]]}']`
  | `timestamp` | timestamps
  | | `['2021-12-09T15:32:59.458Z',..., '2021-11-14T15:32:59.654Z']`
  |`viewIncidenceAngles` | View incidence angles in degrees
  | | `['{"azimuth": {"B02": 288.805, "B03": 291.359, "B04": 293.693, "B08": 290.083, "B05": 294.94, "B06": 296.189, "B07": 297.42, "B8A": 298.651, "B11": 295.947, "B12": 298.873, "B01": 299.813, "B09": 301.042}, "zenith": {"B02": 6.28651, "B03": 6.32378, "B04": 6.3693, "B08": 6.30356, "B05": 6.3982, "B06": 6.43049, "B07": 6.46563, "B8A": 6.50413, "B11": 6.42396, "B12": 6.51144, "B01": 6.54365, "B09": 6.58895}}', '{"azimuth": {"B02": 287.726, "B03": 290.233, "B04": 292.531, "B08": 288.98, "B05": 293.761, "B06": 294.998, "B07": 296.215, "B8A": 297.428, "B11": 294.722, "B12": 297.612, "B01": 298.585, "B09": 299.806}, "zenith": {"B02": 6.39968, "B03": 6.43145, "B04": 6.47169, "B08": 6.41401, "B05": 6.49769, "B06": 6.52711, "B07": 6.55927, "B8A": 6.59462, "B11": 6.52027, "B12": 6.60026, "B01": 6.6314, "B09": 6.67363}}',...,'{"azimuth": {"B02": 287.734, "B03": 290.267, "B04": 292.586, "B08": 289.001, "B05": 293.828, "B06": 295.078, "B07": 296.307, "B8A": 297.531, "B11": 294.8, "B12": 297.717, "B01": 298.699, "B09": 299.93}, "zenith": {"B02": 6.34038, "B03": 6.37228, "B04": 6.41274, "B08": 6.35475, "B05": 6.43893, "B06": 6.46856, "B07": 6.50099, "B8A": 6.53659, "B11": 6.46168, "B12": 6.54227, "B01": 6.57366, "B09": 6.61622}}']}` 
  |`count` | Number of re-visits for the location (not present in the dataset, generated by `items_to_images()`)
  | | 10  

#### Images

  Sentinel-2 carries a Multi-Spectral Instrument (MSI) payload. The data (imagery) produced contains 13 spectral bands across the visible, near-infrared (NIR), and shortwave infrared (SWIR) spectra. The data in the dataset includes these 13 bands. Samples in the dataset contain varied numbers of re-visits per location.

  | key | Product | Image Resolution | Size (pixels) | Bands | Re-samples | Data type |
  |--|--|--|--|--|--|--|
  | 10m | NIR  | 10m | 384 x 384 | 1 | multiple | uint8 |
  | 20m | 20m bands | 20m | 192 x 192 | 6 | multiple | uint8 |  
  | 40m | 60m bands | 10m | 96 x 96 | 2 | multiple | uint8 |  
  | rgb | RGB | 10m | 384 x 384 | 3 | multiple | uint8 |  
  | scl | SCL | 20m | 192 x 192 | 1 | multiple | uint8 |  

Note: Despite data type being stored in uint64, values range between 0 and 255 (uint8).

#### Example (Jupyter Notebook)
<table border="1" style="border-collapse: inherit;" width="100%" bgcolor="#f0f0f0"><tr><td>

```python
import numpy as np
import earthview as ev

data = ev.load_dataset("sentinel_2", shards=[88])  # shard is optional

sample = next(iter(data))

print(sample.keys())
print(np.array(sample['10m']).shape)      
print(np.array(sample['20m']).shape)       
print(np.array(sample['40m']).shape)      
print(np.array(sample['rgb']).shape)       
print(np.array(sample['scl']).shape)   
```
</td></tr></table>
<table border="1" style="border-collapse: inherit;" width="100%" bgcolor="#f0f0f0"><tr><td>

```
dict_keys(['10m', '20m', '40m', 'rgb', 'scl', 'metadata'])
(10, 1, 384, 384)
(10, 6, 192, 192)
(10, 2, 96, 96)
(10, 3, 384, 384)
(10, 1, 192, 192)
```
</td></tr></table>
<table border="1" style="border-collapse: inherit;" width="100%" bgcolor="#f0f0f0"><tr><td>

```python
sample = ev.item_to_images("sentinel_2", sample)
display(sample)
display(sample["10m"][0])
display(sample["rgb"][0])
```
</td></tr></table>
<table border="1" style="border-collapse: inherit;" width="100%" bgcolor="#f0f0f0"><tr><td>

```
{'10m': [<PIL.Image.Image image mode=L size=384x384>,
  <PIL.Image.Image image mode=L size=384x384>,
  <PIL.Image.Image image mode=L size=384x384>,
  <PIL.Image.Image image mode=L size=384x384>,
  <PIL.Image.Image image mode=L size=384x384>,
  <PIL.Image.Image image mode=L size=384x384>,
  <PIL.Image.Image image mode=L size=384x384>,
  <PIL.Image.Image image mode=L size=384x384>,
  <PIL.Image.Image image mode=L size=384x384>,
  <PIL.Image.Image image mode=L size=384x384>],
 '20m': [<PIL.Image.Image image mode=RGB size=192x192>,
  <PIL.Image.Image image mode=RGB size=192x192>,
  <PIL.Image.Image image mode=RGB size=192x192>,
  <PIL.Image.Image image mode=RGB size=192x192>,
  <PIL.Image.Image image mode=RGB size=192x192>,
  <PIL.Image.Image image mode=RGB size=192x192>,
  <PIL.Image.Image image mode=RGB size=192x192>,
  <PIL.Image.Image image mode=RGB size=192x192>,
  <PIL.Image.Image image mode=RGB size=192x192>,
  <PIL.Image.Image image mode=RGB size=192x192>],
 '40m': array([[[[  4,   4,   4, ..., 115, 108, 102],
          [  4,   4,   4, ..., 102,  95,  90],
          [  4,   4,   4, ...,  89,  82,  76],
          ...,
          [ 54,  54,  54, ...,  65,  65,  64],
          [ 54,  55,  56, ...,  63,  62,  62],
          [ 55,  56,  57, ...,  62,  60,  60]]]], dtype=uint8),
 'rgb': [<PIL.Image.Image image mode=RGB size=384x384>,
  <PIL.Image.Image image mode=RGB size=384x384>,
  <PIL.Image.Image image mode=RGB size=384x384>,
  <PIL.Image.Image image mode=RGB size=384x384>,
  <PIL.Image.Image image mode=RGB size=384x384>,
  <PIL.Image.Image image mode=RGB size=384x384>,
  <PIL.Image.Image image mode=RGB size=384x384>,
  <PIL.Image.Image image mode=RGB size=384x384>,
  <PIL.Image.Image image mode=RGB size=384x384>,
  <PIL.Image.Image image mode=RGB size=384x384>],
 'scl': [<PIL.Image.Image image mode=L size=192x192>,
  <PIL.Image.Image image mode=L size=192x192>,
  <PIL.Image.Image image mode=L size=192x192>,
  <PIL.Image.Image image mode=L size=192x192>,
  <PIL.Image.Image image mode=L size=192x192>,
  <PIL.Image.Image image mode=L size=192x192>,
  <PIL.Image.Image image mode=L size=192x192>,
  <PIL.Image.Image image mode=L size=192x192>,
  <PIL.Image.Image image mode=L size=192x192>,
  <PIL.Image.Image image mode=L size=192x192>],
 'metadata': {'s3Path': ['tiles/18/N/VF/2021/12/9/0',
   'tiles/18/N/VF/2021/10/25/0',
   'tiles/18/N/VF/2021/3/14/0',
   'tiles/18/N/VF/2021/6/12/0',
   'tiles/18/N/VF/2021/12/14/0',
   'tiles/18/N/VF/2021/11/19/0',
   'tiles/18/N/VF/2021/12/4/0',
   'tiles/18/N/VF/2021/9/5/0',
   'tiles/18/N/VF/2020/12/9/0',
   'tiles/18/N/VF/2021/11/14/0'],
  'solarAngles': [{'azimuth': 140.394, 'zenith': 30.3633},
   {'azimuth': 124.852, 'zenith': 22.0045},
   {'azimuth': 95.5396, 'zenith': 24.7237},
   {'azimuth': 41.6202, 'zenith': 31.7255},
   {'azimuth': 140.117, 'zenith': 31.0129},
   {'azimuth': 138.058, 'zenith': 26.8774},
   {'azimuth': 140.325, 'zenith': 29.6254},
   {'azimuth': 73.0239, 'zenith': 22.8998},
   {'azimuth': 140.382, 'zenith': 30.3984},
   {'azimuth': 136.448, 'zenith': 25.8697}],
  'tileGeometry': [{'type': 'Polygon',
    'crs': 'epsg:32618',
    'coordinates': [[[430680.0, 4020.0],
      [434520.0, 4020.0],
      [434520.0, 180.0],
      [430680.0, 180.0],
      [430680.0, 4020.0]]]}],
  'timestamp': ['2021-12-09T15:32:59.458Z',
   '2021-10-25T15:33:02.398Z',
   '2021-03-14T15:33:00.373Z',
   '2021-06-12T15:33:01.436Z',
   '2021-12-14T15:32:54.854Z',
   '2021-11-19T15:33:00.970Z',
   '2021-12-04T15:32:56.151Z',
   '2021-09-05T15:32:57.392Z',
   '2020-12-09T15:32:58.492Z',
   '2021-11-14T15:32:59.654Z'],
  'viewIncidenceAngles': [{'azimuth': {'B02': 288.805,
     'B03': 291.359,
     'B04': 293.693,
     'B08': 290.083,
     'B05': 294.94,
     'B06': 296.189,
     'B07': 297.42,
     'B8A': 298.651,
     'B11': 295.947,
     'B12': 298.873,
     'B01': 299.813,
     'B09': 301.042},
    'zenith': {'B02': 6.28651,
     'B03': 6.32378,
     'B04': 6.3693,
     'B08': 6.30356,
     'B05': 6.3982,
     'B06': 6.43049,
     'B07': 6.46563,
     'B8A': 6.50413,
     'B11': 6.42396,
     'B12': 6.51144,
     'B01': 6.54365,
     'B09': 6.58895}},
    ...
   {'azimuth': {'B02': 287.734,
     'B03': 290.267,
     'B04': 292.586,
     'B08': 289.001,
     'B05': 293.828,
     'B06': 295.078,
     'B07': 296.307,
     'B8A': 297.531,
     'B11': 294.8,
     'B12': 297.717,
     'B01': 298.699,
     'B09': 299.93},
    'zenith': {'B02': 6.34038,
     'B03': 6.37228,
     'B04': 6.41274,
     'B08': 6.35475,
     'B05': 6.43893,
     'B06': 6.46856,
     'B07': 6.50099,
     'B8A': 6.53659,
     'B11': 6.46168,
     'B12': 6.54227,
     'B01': 6.57366,
     'B09': 6.61622}}],
  'count': 10}}
```
</td></tr></table>

![Sentinel-2 first band](sentinel_2_0.png)

![Sentinel-2 RGB](sentinel_2_rgb.png)


## Known Issues
* Satellogic timestamps are missing time information, only dates are provided. You can access full Satellogic sample timestamps in our STAC Catalog. Please visit our [Satellogic EarthView website](https://satellogic-earthview.s3.us-west-2.amazonaws.com/index.html) for more information. We additionally provide a notebook showing how to navigate through Satellogic dataset. <a href="https://colab.sandbox.google.com/github/satellogic/satellogic-earthview/blob/main/satellogic_earthview_exploration.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open in google colab"></a>

## Citation

To reference this work in your research or publications, please consider citing:


```
@inproceedings{earthview2025,
                author={Velázquez, Diego and Rodríguez, Pau and Alonso, Sergio and Gonfaus, Josep M. and González, Jordi and, Richarte, Gerardo and Marín, Javier and Bengio, Yoshua and Lacoste, Alexandre},
                booktitle={2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)}, 
                title={EarthView: A Large Scale Remote Sensing Dataset for Self-Supervision}, 
                year={2025}}   
```