courses/2025_Advanced_Earth_Observation/docs/slides.md

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true	uncover	invert	true	false	07.07.2025 / University of Potsdam / Joaquin Gottlebe, Florian Ringel

Artificial Surface Materials at Golm

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Research Question

Which artificial surface material types expanded the most and how much (in m^2) between 2018-2024?

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Definition

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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Workflow

1. Preprocessing of data
2. Create training data
3. Classification
4. Change detection
5. Change analysis

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Classes

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Datasets

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Classification Method

1. Read raster bands
2. Apply PCA to reduce spectral dimensionality
3. Rasterize training polygons into labeled pixels
4. Extract labeled pixels as training samples
5. Train Random Forest on labeled data
6. Classify all pixels using trained model
7. Validate with validation data

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Classification Implementation

• rasterio
• geopandas
• sklearn.decomposition.PCA
• sklearn.ensemble.RandomForestClassifier

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Classification Training Data

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Classification Training Data

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Classification Results

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Classification Confusion Matrix

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Classification Accuracy

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Change Detection Method

\text{change\_value} = \text{old\_value} \times 10 + \text{new\_value}

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Change Detection Results

11 12 13 ... 88

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Change Analysis 2018-2024

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Challenges

• Datasets had different amount of Bands
• Deciding on the right classes
• Classification with Enmap Box, unsatisfactory

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Thank you, for your attention!

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Sources

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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.

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.

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.

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.

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.

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.