1. Earthen Mounds Recognition Using LiDAR Images
Zhiwei Ye
Environment & Resources
ECE 539 Final Project

2. Summary
Native American built earthen mounds for religious, ceremonial, burial, and elite residential purposes.
Goal
Automate the process of mounds recognition for better protection of mounds
Task
Train a model from existing map and locate mounds in area without existing maps
Finished
data processing + model selection + cross validation of existing dataset
Unfinished
test on data not used for training

3. Data Hillshade image derived from LiDAR DEM
(from WisconsinView, UW-Madison)
Training dataset over Madison and Four Lakes area (Birmingham, 2009)
Window size for a single instance is 48 pixels by 48 pixels (48m by 48 m)

4. Method
Feature extraction using a pre-trained Convolutional Neural Network:
Inception v3 (Szegedy et al., 2015)
Principle Component Analysis for dimensionality reduction
T-SNE for visualization (Maaten & Hinton, 2008)
Random Forests, Support Vector Machine
Tools: GIS + Python + TensorFlow + Sci-kit Learn
Last hidden layer: Pool3
All 2048 features or n PCA components
Supervised classification

5. Results

6. Discussion Ensemble method integrating different classifiers
Add DEM as input data
The model should be applied to other location other than Madison and Four Lakes area
Train an artificial neural network from scratch
Reference
Birmingham, Robert A. Spirits of earth: The effigy mound landscape of Madison and the Four Lakes. Univ of Wisconsin Press, 2009.
Birmingham, Robert A., and Amy L. Rosebrough. Indian mounds of Wisconsin. University of Wisconsin Pres, 2017.
Szegedy, Christian, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. "Rethinking the inception architecture for computer vision." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp
Maaten, Laurens van der, and Geoffrey Hinton. "Visualizing data using t-SNE." Journal of machine learning research 9, no. Nov (2008):