> For the complete documentation index, see [llms.txt](https://terradapt.gitbook.io/terradapt-cascadia-documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://terradapt.gitbook.io/terradapt-cascadia-documentation/methods-and-validation/landcover/training-data.md). # Training Data For each class in our landcover taxonomy, we created between 500 and 5000 observations divided among the years 2008, 2011, 2013, 2016, 2019, and 2021 based on the consensus among the following landcover-related datasets:
DatasetCitationData availability
Dynamic World v1Brown et al. 20222015-present
LCMAP v1.3Xian et al. 20221985-2021
US National Landcover Database Homer et al. 2020
Rangeland Analysis PlatformJones et al. 2018
JRC Global Surface WaterPekel et al. 2016
USDA CroplandCROSBoryan et al. 2011
Canada Annual Crop InventoryFizette et al. 2013
North American Land Change Monitoring System
ESA WorldCover
More observations were produced for more common landcover classes compared to rare classes. Not all datasets were available for every year across the full extent of the region. Also, the landcover datasets in the table above only partially conformed to our landcover taxonomy, and some landcover classes were widely included in these datasets while others were only available in a small subset. Given these limitations, there were a variable number of datasets that were available to assess model agreement depending on the class, location, and year. However, in all cases, at least 2 datasets agreed on the class of the observation in our training dataset. The total number of training data samples was 78,500. For each observation of a consensus landcover class, our Google Earth Engine (GEE) based workflow extracted the value of all [covariates](/terradapt-cascadia-documentation/methods-and-validation/landcover/covariates.md) matched to the year corresponding to the sampling year of the consensus landcover observation. The result was a table (a GEE FeatureCollection) with attributes for the consensus landcover class and all covariates. All training data locations were assigned a 'fold' (value from 1 to 10) that was used to reduce autocorrelation during model validation. Folds were assigned based on a 10 km x 10 km grid of randomized folds across the region. During model training, one fold of data was always withheld, and because of the spatial assignment of folds, the withheld data was spatially separated from all other folds and therefore expected to be a more independent assessment compared to validation based on folds that were spatially intermixed. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://terradapt.gitbook.io/terradapt-cascadia-documentation/methods-and-validation/landcover/training-data.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.