177 lines
3.8 KiB
Markdown
177 lines
3.8 KiB
Markdown
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footer: '07.07.2025 / University of Potsdam / Joaquin Gottlebe, Florian Ringel'
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# Artificial Surface Materials at Golm
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---
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##### Research Question
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Which artificial surface material types expanded the most and how much (in $m^2$) between 2018-2024?
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##### Definition
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**Artifical Surface Materials**: Materials, that are located on the surface and are either Man-made or deliberately placed there by humans.
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##### Study area Campus Golm
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---
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##### Workflow
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1. Preprocessing of data
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2. Create training data
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2. Classification
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3. Change detection
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4. Change analysis
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---
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##### Classes
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##### Datasets
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##### Classification Method
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1. Read raster **bands**
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2. Apply **PCA** to reduce spectral dimensionality
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3. Rasterize training polygons into **labeled pixels**
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4. Extract labeled pixels as training samples
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5. Train **Random Forest** on labeled data
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6. Classify all pixels using trained model
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7. Validate with validation data
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##### Classification Implementation
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- rasterio
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- geopandas
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- sklearn.decomposition.PCA
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- sklearn.ensemble.RandomForestClassifier
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##### Classification Training Data
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---
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##### Classification Training Data
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---
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##### Classification Results
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---
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##### Classification Confusion Matrix
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---
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##### Classification Accuracy
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---
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##### Change Detection Method
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$$
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\text{change\_value} = \text{old\_value} \times 10 + \text{new\_value}
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$$
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---
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##### Change Detection Results
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11
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12
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13
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...
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88
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---
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##### Change Analysis 2018-2024
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---
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##### Challenges
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- Datasets had different amount of Bands
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- Deciding on the right classes
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- Classification with Enmap Box, unsatisfactory
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---
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Thank you, for your attention!
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---
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##### Sources
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<style scoped>
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section {
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font-size: 20px;
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}
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</style>
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Balsamo et al. “Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review.” Remote Sensing 10, no. 12 (December 2018): 2038. https://doi.org/10.3390/rs10122038.
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Guanter et al. “The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation.” Remote Sensing 7, no. 7 (July 2015): 8830–57. https://doi.org/10.3390/rs70708830.
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Ilehag et al. “KLUM: An Urban VNIR and SWIR Spectral Library Consisting of Building Materials.” Remote Sensing 11, no. 18 (January 2019): 2149. https://doi.org/10.3390/rs11182149.
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Lindeni et al. “Imaging Spectroscopy of Urban Environments.” Surveys in Geophysics 40, no. 3 (May 1, 2019): 471–88. https://doi.org/10.1007/s10712-018-9486-y.
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Okujeni et al. “Support Vector Regression and Synthetically Mixed Training Data for Quantifying Urban Land Cover.” Remote Sensing of Environment 137 (October 1, 2013): 184–97. https://doi.org/10.1016/j.rse.2013.06.007.
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Zhao et al. “ASI: An Artificial Surface Index for Landsat 8 Imagery.” International Journal of Applied Earth Observation and Geoinformation 107 (March 1, 2022): 102703. https://doi.org/10.1016/j.jag.2022.102703.
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