Conference Presentations by Nina Isabella Hofer
Large scale airborne laser scanning is still gaining popularity as a method in many disciplines, ... more Large scale airborne laser scanning is still gaining popularity as a method in many disciplines, including archaeology. In the last few years, the creation and use of different visualization methods to identify archaeological patterns in high-resolution terrain data has increased in importance. Since technologies are developing rapidly and enabling large data acquisition, archaeologists can benefit by applying machine learning approaches to handling such big datasets. By using methods from different fields of knowledge, we explore the potential of topographic data for automated landform and feature classifications, instead of using their visualizations for optical interpretation. We therefore use techniques from geomorphology and morphometry to create topographic datasets, which are the basis for feature classifications using and comparing supervised machine learning and neural network algorithms. By using our 2015 ALS datasets from Cambodia, we are focusing on tropical landscapes and distinct morphologies, to explore 1) structures that can be classified automatically in those geographical conditions and 2) applicable geoscientific approaches.
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Using ALS to Uncover, Map and Analyse Archaeological Topography in Mainland Southeast Asia: An Ov... more Using ALS to Uncover, Map and Analyse Archaeological Topography in Mainland Southeast Asia: An Overview
Nina Hofer & Damian Evans
In recent years airborne laser scanning (ALS) data have become a valuable addition to the existing archive of remote sensing datasets covering the landscapes of the Angkorian civilisation (~9 th to 15 th centuries AD). At present we have very high-resolution ALS data over all of the major temple complexes in Cambodia, the heartland of the Khmer Empire, and the process of acquiring similar data in neighbouring countries is currently underway. For the first time in studies of early Southeast Asia, we have very consistent, comparable, high-quality datasets covering archaeological landscapes across a diverse range of environments, time periods, and types of human activity such as urbanisation, agriculture and industry. This has offered us the opportunity to explore and experiment with different processing and classification algorithms in order to optimise the ALS-derived imagery for archaeological analysis and interpretation, and to create workflows that are tailored to specific areas and objectives. In this paper we present the mapping work that's currently underway at a number of sites across Cambodia and examine all of these issues in detail, and consider current limitations and future opportunities for the application of ALS in tropical forest environments in Southeast Asia and beyond.
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Books by Nina Isabella Hofer
Thesis, 2018
Using Airborne laser scanning (or LiDAR) for archaeological large-scale prospection of topography... more Using Airborne laser scanning (or LiDAR) for archaeological large-scale prospection of topography is especially interesting in forested regions where very subtle earthen archaeological features lie beneath the canopy, as at Phnom Kulen Mountain Plateau in Cambodia. In this thesis, the Phnom Kulen LiDAR data from the Cambodian Archaeological Lidar Initiative are processed for terrain analysis and visualization of archaeological topography. For systematic interpretation of these data, suitable visualization is necessary to allow a human interpreter the identification and mapping of archaeological topography. The key to successfully using various approaches in visualization of terrain datasets is to know the influences, advantages and disadvantages of different methods and how they contribute to identification and mapping of different topographical feature types. A comparison of previous studies on visualization techniques from ALS data shows, that there is no single visualization method which outperforms the rest in all types of terrain and for all types of features. Results of these studies are used to build hypotheses about suitability of different visualizations for feature identification and mapping in the study area. These hypotheses are examined at four representative test areas which include mounds, ponds, embankments and channels. All these archaeological microreliefs are extremely subtle and therefore hard to identify. Thus, besides analyzing which visualizations can be used to potentially identify and map them, the author presents the results of two analytical assessments in the study area. A range of quintessential methods were compared, which are: Standard hillshade, combined hillshades using principal-component-analysis, sky-view-factor, openness, slope, local relief model, local dominance and curvature (Laplacian-of-gaussian). The first assessment is based on a comparison of gradients of different visualization methods and spatial analysis using different terrain properties. It is analyzed under which circumstances-like different topographical features and terrain properties-different methods provide the highest gradients and therefore best perception of feature identification. In the second assessment the visualizations are used separately for mapping features and comparing the results. It is examined which visualizations best allow for identification and exact delineation of different archaeological topography. For linear earthworks the feature length of the mapping is compared, while for areal earthworks the difference in area of two mapping strategies (tight vs. loose) is measured. Those analyses show that the assumed hypotheses are not always true and thus results from previous studies are not transferable to this case study. However, relief manipulation like local relief models and visualizations based on openness and sky-view-factor are proven most suitable for identification and mapping of archaeological topography in the study area, depending on local terrain properties and feature types.
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Papers by Nina Isabella Hofer
Antiquity, 2019
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Conference Presentations by Nina Isabella Hofer
Nina Hofer & Damian Evans
In recent years airborne laser scanning (ALS) data have become a valuable addition to the existing archive of remote sensing datasets covering the landscapes of the Angkorian civilisation (~9 th to 15 th centuries AD). At present we have very high-resolution ALS data over all of the major temple complexes in Cambodia, the heartland of the Khmer Empire, and the process of acquiring similar data in neighbouring countries is currently underway. For the first time in studies of early Southeast Asia, we have very consistent, comparable, high-quality datasets covering archaeological landscapes across a diverse range of environments, time periods, and types of human activity such as urbanisation, agriculture and industry. This has offered us the opportunity to explore and experiment with different processing and classification algorithms in order to optimise the ALS-derived imagery for archaeological analysis and interpretation, and to create workflows that are tailored to specific areas and objectives. In this paper we present the mapping work that's currently underway at a number of sites across Cambodia and examine all of these issues in detail, and consider current limitations and future opportunities for the application of ALS in tropical forest environments in Southeast Asia and beyond.
Books by Nina Isabella Hofer
Papers by Nina Isabella Hofer
Nina Hofer & Damian Evans
In recent years airborne laser scanning (ALS) data have become a valuable addition to the existing archive of remote sensing datasets covering the landscapes of the Angkorian civilisation (~9 th to 15 th centuries AD). At present we have very high-resolution ALS data over all of the major temple complexes in Cambodia, the heartland of the Khmer Empire, and the process of acquiring similar data in neighbouring countries is currently underway. For the first time in studies of early Southeast Asia, we have very consistent, comparable, high-quality datasets covering archaeological landscapes across a diverse range of environments, time periods, and types of human activity such as urbanisation, agriculture and industry. This has offered us the opportunity to explore and experiment with different processing and classification algorithms in order to optimise the ALS-derived imagery for archaeological analysis and interpretation, and to create workflows that are tailored to specific areas and objectives. In this paper we present the mapping work that's currently underway at a number of sites across Cambodia and examine all of these issues in detail, and consider current limitations and future opportunities for the application of ALS in tropical forest environments in Southeast Asia and beyond.