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Automated detection of former field systems from airborne laser scanning data: a new approach for Historical Ecology
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2021-10-12 , DOI: 10.1016/j.jag.2021.102563
P.-A. Herrault 1 , Q. Poterek 1 , B. Keller 1 , D. Schwartz 1 , D. Ertlen 1
Affiliation  

Former field systems (FFS) are the most widespread traces of ancient activities in present European landscapes and represent significant perturbations to ecosystems. Through its ability to penetrate forest canopies and detect microlandforms, Airborne Laser Scanning data reveal archaeological relics over large areas, from periods older than the first available Historical Topographic Maps. Mapping these traces from ALS-derived data (e.g. Digital Elevation Model (DEM)) thus allows for a determination of a new temporal baseline in order to evaluate the effects of a longer history on current patterns of biodiversity. Here, we evaluate the ability of traditional machine learning (Random Forest-RF) and deep learning (Fully Connected Networks-FCN) models to detect Medieval Terraced slopes and Ridges and Furrows (RaF) from an ALS-derived DEM in the southern Vosges (1462 km2). We used a combination of Local Binary Patterns and topographical metrics to measure properties of FFS and to train detection models. We then assessed the relative performance of each model semantically and spatially. Our results demonstrated the high suitability of our approach for reproducing major trends in the landscape with a high level of similarity between the predicted and reference spatial patterns (Structural Similarity Index - SSIM > 0.75). RF outperformed FCN for Terraced Slopes, whilst minimizing the false positive rate. FCN slightly outperformed RF for the RaF dataset but showed promising abilities to survey unseen data with a low sensitivity to annotation errors. We suggest that this approach has the potential to offer new spatio-temporal possibilities in Historical Ecology studies as a means of automatically detecting past archaeological ecosystems from a landscape to a regional scale.



中文翻译:

从机载激光扫描数据自动检测以前的现场系统:历史生态学的新方法

前田地系统 (FFS) 是目前欧洲景观中最广泛的古代活动痕迹,代表了对生态系统的重大扰动。通过其穿透森林冠层和探测微地形的能力,机载激光扫描数据揭示了大面积的考古遗迹,其年代早于第一个可用的历史地形图。因此,从 ALS 衍生数据(例如数字高程模型 (DEM))绘制这些痕迹可以确定新的时间基线,以便评估更长的历史对当前生物多样性模式的影响。这里,2)。我们使用局部二进制模式和地形指标的组合来测量 FFS 的属性并训练检测模型。然后,我们在语义和空间上评估了每个模型的相对性能。我们的结果表明,我们的方法非常适合再现景观中的主要趋势,预测和参考空间模式之间具有高度的相似性(结构相似性指数 - SSIM>0.75)。RF 在 Terraced Slopes 上的表现优于 FCN,同时将误报率降至最低。FCN 在 RaF 数据集上的表现略优于 RF,但显示出有前景的能力,能够以对注释错误的低敏感性来调查看不见的数据。我们认为,这种方法有可能在历史生态学研究中提供新的时空可能性,作为一种从景观到区域尺度自动检测过去考古生态系统的手段。

更新日期:2021-10-12
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