Smogseer: A Convolutional LSTM for forecasting air quality from Sentinel-5P data

<h1><strong>Training and checkpoint datasets&nbsp;</strong></h1> <h2><a href="#h_1501865622611722161313186" target="_blank" rel="noopener">S5PL2_5D.nc</a></h2> <p>This is the Sentinel-5P traning dataset for the Smogseer ConvLSTM model. The dataset was created using the xcube Sentinel Hub data store from the <a href="https://deepesdl.readthedocs.io/en/latest/datasets/datastores/#xcube-sentinel-hub-data-store">Deep Earth System Data Lab</a>.</p> <p>The dataset &nbsp;has the following characteristics:</p> <ul> <li>bbox= [68.137207,24.886436,84.836426,34.379713] #WGS84 // lon,lat,lon,lat</li> <li>res = (bbox[2]-bbox[0])/512 # ~3629m</li> <li>date_range = ['2019-01-01', '2023-12-31']</li> <li> <div>timesteps = '5D'</div> </li> </ul> <h2><a href="#h_4062378263291722161323566">X_val.npy</a></h2> <p>Validation feature data with shape: (74, 1, 291, 512, 6)</p> <ul> <li>74: dates</li> <li>1: time steps</li> <li>291: latitudes</li> <li>512: longitudes</li> <li>6: Features ['SO2', 'NO2', 'CH4', 'O3', 'CO', 'HCHO']</li> </ul> <h2><a href="#h_9861938573981722161330905">Y_val.npy</a></h2> <p>Validation target data with shape: (74, 1, 291, 512, 1)</p> <ul> <li>74: dates</li> <li>1: time steps</li> <li>291: latitudes</li> <li>512: longitudes</li> <li>6: Features ['SO2', 'NO2', 'CH4', 'O3', 'CO', 'HCHO']</li> </ul> <h2><a href="#h_321677095651722161353847">smogseer50.keras</a></h2> <p>Model weights for training the ConvLSTM with 50 epochs.</p> <h2><a href="#h_5931467847131722161401921">smogseer100.keras</a></h2> <p>Model weights for training the ConvLSTM with 10 epochs.</p>