Land cover describes the surface of the earth. Land-cover maps are useful in urban planning, resource management, change detection, agriculture, and a variety of other applications in which information related to the earth's surface is required. Land-cover classification is a complex exercise and is difficult to capture using traditional means. Deep learning models are highly capable of learning these complex semantics and can produce superior results.There are a few public datasets for land cover, but the spatial and temporal coverage of these public datasets may not always meet the user’s requirements. It is also difficult to create datasets for a specific time, as it requires expertise and time. Use this deep learning model to automate the manual process and reduce the required time and effort significantly.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.Input8-bit, 3-band very high-resolution (10 cm) imagery.OutputClassified raster with the 8 classes as in the LA county landcover dataset.Applicable geographiesThe model is expected to work well in the United States and will produce the best results in the urban areas of California.Model architectureThis model uses the UNet model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an overall accuracy of 84.8%. The table below summarizes the precision, recall and F1-score of the model on the validation dataset: ClassPrecisionRecallF1 ScoreTree Canopy0.8043890.8461520.824742Grass/Shrubs0.7199930.6272780.670445Bare Soil0.89270.9099580.901246Water0.9808850.9874990.984181Buildings0.9222020.9450320.933478Roads/Railroads0.8696370.8629210.866266Other Paved0.8114650.8119610.811713Tall Shrubs0.7076740.6382740.671185Training dataThis model has been trained on very high-resolution Landcover dataset (produced by LA County).LimitationsSince the model is trained on imagery of urban areas of LA County it will work best in urban areas of California or similar geography.Model is trained on limited classes and may lead to misclassification for other types of LULC classes.Sample resultsHere are a few results from the model.

Land cover describes the surface of the earth. Land cover maps are useful in urban planning, resource management, change detection, agriculture, and a variety of other applications in which information related to earth surface is required. Land cover classification is a complex exercise and is hard to capture using traditional means. Deep learning models are highly capable of learning these complex semantics, giving superior results.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.InputRaster, mosaic dataset, or image service. (Preferred cell size is 10 meters.)Note: This model is trained to work on Sentinel-2 Imagery datasets which are in WGS 1984 Web Mercator (auxiliary sphere) coordinate system (WKID 3857).OutputClassified raster with the same classes as in Corine Land Cover (CLC) 2018.Applicable geographiesThis model is expected to work well in Europe and the United States.Model architectureThis model uses the UNet model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an overall accuracy of 82.41% with Level-1C imagery and 84.0% with Level-2A imagery, for CLC class level 2 classification (15 classes). The table below summarizes the precision, recall and F1-score of the model on the validation dataset.ClassLevel-2A ImageryLevel-1C ImageryPrecisionRecallF1 ScorePrecisionRecallF1 ScoreUrban fabric0.810.830.820.820.840.83Industrial, commercial and transport units0.740.650.690.730.660.7Mine, dump and construction sites0.630.520.570.690.550.61Artificial, non-agricultural vegetated areas0.700.460.550.670.470.55Arable land0.860.900.880.860.890.87Permanent crops0.760.730.740.750.710.73Pastures0.750.710.730.740.710.73Heterogeneous agricultural areas0.610.560.580.620.510.56Forests0.880.930.900.880.920.9Scrub and/or herbaceous vegetation associations0.740.690.720.730.670.7Open spaces with little or no vegetation0.870.840.850.850.820.84Inland wetlands0.810.780.800.820.770.79Maritime wetlands0.740.760.750.870.890.88Inland waters0.940.920.930.940.910.92Marine waters0.980.990.980.970.980.98This model has an overall accuracy of 90.79% with Level-2A imagery for CLC class level 1 classification (5 classes). The table below summarizes the precision, recall and F1-score of the model on the validation dataset.ClassPrecisionRecallF1 ScoreArtificial surfaces0.850.810.83Agricultural areas0.900.910.91Forest and semi natural areas0.910.920.92Wetlands0.770.700.73Water bodies0.960.970.96Training dataThis model has been trained on the Corine Land Cover (CLC) 2018 with the same Sentinel 2 scenes that were used to produce the database. Scene IDs for the imagery were available in the metadata of the dataset.Sample resultsHere are a few results from the model. To view more, see this story.

Land cover describes the surface of the earth. Land cover maps are useful in urban planning, resource management, change detection, agriculture, and a variety of other applications in which information related to earth surface is required. Land cover classification is a complex exercise and is hard to capture using traditional means. Deep learning models are highly capable of learning these complex semantics, giving superior results.Licensing requirementsArcGIS Desktop – ArcGIS Image Analyst extension for ArcGIS ProArcGIS Enterprise – ArcGIS Image Server with raster analytics configuredArcGIS Online – ArcGIS Image for ArcGIS OnlineUsing the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.InputRaster, mosaic dataset, or image service. (Preferred cell size is 10 meters.)Note: This model is trained to work on Sentinel-2 Imagery datasets which are in WGS 1984 Web Mercator (auxiliary sphere) coordinate system (WKID 3857).OutputClassified raster with the same classes as in Corine Land Cover (CLC) 2018.Applicable geographiesThis model is expected to work well in Europe and the United States.Model architectureThis model uses the UNet model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an overall accuracy of 82.41% with Level-1C imagery and 84.0% with Level-2A imagery, for CLC class level 2 classification (15 classes). The table below summarizes the precision, recall and F1-score of the model on the validation dataset.ClassLevel-2A ImageryLevel-1C ImageryPrecisionRecallF1 ScorePrecisionRecallF1 ScoreUrban fabric0.810.830.820.820.840.83Industrial, commercial and transport units0.740.650.690.730.660.7Mine, dump and construction sites0.630.520.570.690.550.61Artificial, non-agricultural vegetated areas0.700.460.550.670.470.55Arable land0.860.900.880.860.890.87Permanent crops0.760.730.740.750.710.73Pastures0.750.710.730.740.710.73Heterogeneous agricultural areas0.610.560.580.620.510.56Forests0.880.930.900.880.920.9Scrub and/or herbaceous vegetation associations0.740.690.720.730.670.7Open spaces with little or no vegetation0.870.840.850.850.820.84Inland wetlands0.810.780.800.820.770.79Maritime wetlands0.740.760.750.870.890.88Inland waters0.940.920.930.940.910.92Marine waters0.980.990.980.970.980.98This model has an overall accuracy of 90.79% with Level-2A imagery for CLC class level 1 classification (5 classes). The table below summarizes the precision, recall and F1-score of the model on the validation dataset.ClassPrecisionRecallF1 ScoreArtificial surfaces0.850.810.83Agricultural areas0.900.910.91Forest and semi natural areas0.910.920.92Wetlands0.770.700.73Water bodies0.960.970.96Sample ResultsHere are a few results from the model. To view more, see this story.