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Source: Brian Johnson
Data Set Information: This dataset was derived from geospatial data from two sources: 1) Landsat time-series satellite imagery from the years 2014-2015, and 2) crowdsourced georeferenced polygons with land cover labels obtained from OpenStreetMap. The crowdsourced polygons cover only a small part of the image area, and are used used to extract training data from the image for classifying the rest of the image. The main challenge with the dataset is that both the imagery and the crowdsourced data contain noise (due to cloud cover in the images and innaccurate labeling/digitizing of polygons).
Attribute Information: class: The land cover class (impervious, farm, forest, grass, orchard, water) [note: this is the target variable to classify].
Relevant Papers: Johnson, B. A., & Iizuka, K. (2016). Integrating OpenStreetMap crowdsourced data and Landsat time-series imagery for rapid land use/land cover (LULC) mapping: Case study of the Laguna de Bay area of the Philippines. Applied Geography, 67, 140-149. Citation Request: Please cite: Johnson, B. A., & Iizuka, K. (2016). Integrating OpenStreetMap crowdsourced data and Landsat time-series imagery for rapid land use/land cover (LULC) mapping: Case study of the Laguna de Bay area of the Philippines. Applied Geography, 67, 140-149. |
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