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+---
+marp: true
+theme: uncover
+class: invert
+paginate: true
+_paginate: false
+footer: '07.07.2025 / University of Potsdam / Joaquin Gottlebe, Florian Ringel'
+---
+
+<!-- _paginate: skip -->
+# Artificial Surface Materials at Golm
+
+---
+
+##### Research Question
+
+Which artificial surface material types expanded the most and how much (in $m^2$) between 2018-2024?
+
+---
+
+##### Definition
+
+**Artifical Surface Materials**: Materials, that are located on the surface and are either Man-made or deliberately placed there by humans.
+
+---
+
+##### Study area Campus Golm
+
+![w:550](../data/golm.png)
+
+---
+
+##### Workflow
+
+1. Preprocessing of data
+2. Create training data
+2. Classification
+3. Change detection 
+4. Change analysis
+
+---
+
+##### Classes
+
+![w:600](../results/metadata.png)
+
+---
+
+##### Datasets
+
+![100%](../results/empty.png)
+![bg 100%](../results/meta_2018.png)
+![bg 100%](../results/meta_2024.png)
+
+---
+
+##### 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
+
+---
+
+##### Classification Implementation
+
+- rasterio
+- geopandas
+- sklearn.decomposition.PCA
+- sklearn.ensemble.RandomForestClassifier
+
+---
+##### Classification Training Data
+
+![100%](../results/empty.png)
+![bg 100%](../results/training_2018_meta.png)
+![bg 100%](../results/training2018.png)
+
+---
+
+##### Classification Training Data
+
+![100%](../results/empty.png)
+![bg 100%](../results/training_2024_meta.png)
+![bg 100%](../results/training2024.png)
+
+---
+
+##### Classification Results
+
+![100%](../results/empty.png)
+![bg 100%](../results/classified_2018.png)
+![bg 100%](../results/classified_2024.png)
+
+---
+
+##### Classification Confusion Matrix
+
+
+![100%](../results/empty.png)
+![bg 100%](../results/confusion_matrix_2018.png)
+![bg 100%](../results/confusion_matrix_2024.png)
+
+---
+
+##### Classification Accuracy
+
+![100%](../results/empty.png)
+![bg 100%](../results/accuracy_2018.png)
+![bg 100%](../results/accuracy_2024.png)
+
+---
+
+##### Change Detection Method
+
+$$
+\text{change\_value} = \text{old\_value} \times 10 + \text{new\_value}
+$$
+
+---
+
+##### Change Detection Results
+
+11
+12
+13
+...
+88
+
+![bg right:50% 100%](../results/change.png)
+
+--- 
+
+##### Change Analysis 2018-2024
+
+![w:750](../results/difference.png)
+
+---
+
+##### Challenges
+
+- Datasets had different amount of Bands
+- Deciding on the right classes
+- Classification with Enmap Box, unsatisfactory
+
+---
+
+Thank you, for your attention!
+
+---
+
+##### Sources
+
+<style scoped>
+section {
+    font-size: 20px;
+}
+</style>
+
+
+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.
+