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Award ID contains: 1822110

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  1. While archaeologists have long understood that thermal and multi-spectral imagery can potentially reveal a wide range of ancient cultural landscape features, only recently have advances in drone and sensor technology enabled us to collect these data at sufficiently high spatial and temporal resolution for archaeological field settings. This paper presents results of a study at the Enfield Shaker Village, New Hampshire (USA), in which we collect a time-series of multi-spectral visible light, near-infrared (NIR), and thermal imagery in order to better understand the optimal contexts and environmental conditions for various sensors. We present new methods to remove noise from imagery and to combine multiple raster datasets in order to improve archaeological feature visibility. Analysis compares results of aerial imaging with ground-penetrating radar and magnetic gradiometry surveys, illustrating the complementary nature of these distinct remote sensing methods. Results demonstrate the value of high-resolution thermal and NIR imagery, as well as of multi-temporal image analysis, for the detection of archaeological features on and below the ground surface, offering an improved set of methods for the integration of these emerging technologies into archaeological field investigations. 
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  2. null (Ed.)
    While archaeologists have long understood that thermal and multi-spectral imagery can potentially reveal a wide range of ancient cultural landscape features, only recently have advances in drone and sensor technology enabled us to collect these data at sufficiently high spatial and temporal resolution for archaeological field settings. This paper presents results of a study at the Enfield Shaker Village, New Hampshire (USA), in which we collect a time-series of multi-spectral visible light, near-infrared (NIR), and thermal imagery in order to better understand the optimal contexts and environmental conditions for various sensors. We present new methods to remove noise from imagery and to combine multiple raster datasets in order to improve archaeological feature visibility. Analysis compares results of aerial imaging with ground-penetrating radar and magnetic gradiometry surveys, illustrating the complementary nature of these distinct remote sensing methods. Results demonstrate the value of high-resolution thermal and NIR imagery, as well as of multi-temporal image analysis, for the detection of archaeological features on and below the ground surface, offering an improved set of methods for the integration of these emerging technologies into archaeological field investigations 
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  3. null (Ed.)
    This study presents a new approach for detection and mapping of ancient slag heaps using 16-band multispectral satellite imagery. Understanding the distribution of slag (a byproduct of metal production) is of great importance for understanding how metallurgy shaped long-term economic and political change across the ancient Near East. This study presents results of slag mapping in Oman using WorldView-3 (WV3) satellite imagery. A semi-automated target detection routine using a mixed tuned matched filtering (MTMF) algorithm with scene-derived spectral signatures was applied to 16-band WV3 imagery. Associated field mapping at two copper production sites indicates that WorldView-3 satellite data can differentiate slag and background materials with a relatively high (>90%) overall accuracy. Although this method shows promise for future initiatives to discover and map slag deposits, difficulties in dark object spectral differentiation and underestimation of total slag coverage substantially limit its use. Resulting lower estimations of combined user’s (61%) and producer’s (45%) accuracies contextualize these limitations for slag specific classification. Accordingly, we describe potential approaches to address these challenges in future studies. As sites of ancient metallurgy in Oman are often located in areas of modern exploration and mining, detection and mapping of ancient slag heaps via satellite imagery can be helpful for discovery and monitoring of vulnerable cultural heritage sites. 
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  4. null (Ed.)