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The Efficacy and Analytical Importance of Manual Feature Extraction Using Lidar Datasets

  • Seth Quintus (a1), Stephanie S. Day (a2) and Nathan J. Smith (a3)


The availability of lidar datasets has led to several advances in archaeology, notably in the process of site prospection. Some remote sensing practitioners have aimed to create automated feature extraction (AFE) techniques that increase the efficiency and efficacy of identification and analysis. While these advances have been successful, many archaeological professionals who might have an interest in lidar-derived products do not have the technical experience to modify or create AFE techniques for particular regions or environments. Additionally, some features are not appropriate for AFE. Instead, the most widely used technique is still likely to be visually based manual feature identification. Using authors of different experience levels, we seek to evaluate the use of manual techniques for feature identification and subsequent analysis by implementing a publicly available lidar-derived digital elevation model (DEM). We demonstrate that manual feature extraction (MFE) can be accurate when more than one researcher is involved in a sort of “checks and balances” process. We also show that the use of confidence ratings can be an important part of this process if those ratings have some systematic and clearly defined underpinning. Finally, we argue, using a case study from American Samoa, that manually identified features can be analytically important as part of larger landscape studies.

La disponibilidad de conjuntos de datos lídar ha permitido varios avances en arqueología, notablemente en el proceso de prospección de sitios. Algunos profesionales de teledetección han apuntado a crear técnicas de extracción de características automatizadas (AFE por sus siglas en inglés) que aumentan la eficiencia y eficacia de la identificación y análisis. Aun cuando estos avances han sido exitosos, muchos arqueólogos interesados en el conjunto de datos lídar no tienen la experiencia técnica para modificar o crear técnicas AFE para su uso en regiones o ambientes particulares. Adicionalmente, algunos rasgos podrían no ser apropiados para el uso de AFE. Por lo tanto, es probable que la técnica mayormente usada continúe siendo la identificación manual de características por medio visual. Usando tres autores con diferentes niveles de experiencia, buscamos evaluar el uso de técnicas manuales para la identificación de rasgos y análisis subsecuentes usando un modelo de elevación digital de acceso público derivado de datos lídar. Demostramos que la extracción manual de características (MFE por sus siglas en inglés) puede ser precisa cuando más de un investigador participa en una especie de sistema de controles y balances. Demostramos que el uso de índices de confianza puede ser una parte importante de este proceso si las clasificaciones tienen bases claramente definidas y sistemáticas. Finalmente, usando el estudio de un caso de Samoa Estadounidense, argumentamos que la identificación manual de características puede ser analíticamente importante como parte de estudios de paisaje más amplios.



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The Efficacy and Analytical Importance of Manual Feature Extraction Using Lidar Datasets

  • Seth Quintus (a1), Stephanie S. Day (a2) and Nathan J. Smith (a3)


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