BUSCADOR RECOLECTA

JavaScript code to be implemented in Google Earth Engine(c) for large-scale, multi-temporal remote sensing of palaeo-river networks.

This research presents a seasonal multi-temporal approach to the detection of palaeo-rivers over large areas based on long-term vegetation dynamics and spectral decomposition techniques. Twenty-eight years of Landsat 5 data, a total of 1711 multi-spectral images, have been bulk processed using Google Earth Engine© Code Editor and cloud computing infrastructure.

JavaScript code to be implemented in Google Earth Engine(c) for automated machine learning-based potsherd detection using high-resolution drone imagery.

This research presents the first proof of concept for the automated recording of material culture dispersion across large areas using high resolution drone imagery, photogrammetry and a combination of machine learning and geospatial analysis that can be run using the Google Earth Engine geospatial cloud computing platform.

JavaScript code to be implemented in Google Earth Engine(c) for Hybrid MSRM-Based Deep Learning and Multitemporal Sentinel 2-Based Machine Learning Algorithm.

Algorithm for large-scale automatic detection of burial mounds, one of the most common types of archaeological sites globally, using LiDAR and multispectral satellite data. Although previous attempts were able to detect a good proportion of the known mounds in a given area, they still presented high numbers of false positives and low precision values. Our proposed approach combines random forest for soil classification using multitemporal multispectral Sentinel-2 data and a deep learning model using YOLOv3 on LiDAR data previously pre-processed using a multi–scale relief model. The resulting algorithm significantly improves previous attempts with a detection rate of 89.5%, an average precision of 66.75%, a recall value of 0.64 and a precision of 0.97, which allowed, with a small set of training data, the detection of 10,527 burial mounds over an area of near 30,000 km2, the largest in which such an approach has ever been applied. The open code and platforms employed to develop the algorithm allow this method to be applied anywhere LiDAR data or high-resolution digital terrain models are available.

JavaScript code to be implemented in Google Earth Engine(c).

The multi-scale relief model (MSRM) is a new algorithm for the visual interpretation of landforms using DSMs. The significance of this new method lies in its capacity to extract landform morphology from both high- and low-resolution DSMs independently of the shape or scale of the landform under study. This method thus provides important advantages compared to previous approaches as it: (1) allows the use of worldwide medium resolution models, such as SRTM, ASTER GDEM, ALOS, and TanDEM-X; (2) offers an alternative to traditional photograph interpretation that does not rely on the quality of the imagery employed nor on the environmental conditions and time of its acquisition; and (3) can be easily implemented for large areas using traditional GIS/RS software.

The algorithm is tested in the Sutlej-Yamuna interfluve, which is a very large low-relief alluvial plain in northwest India where 10 000 km of palaeoriver channels have been mapped using MSRM.

These folders store the results of the automated extraction of individual potsherd fragments in the soil surface provided by two different approaches. They both used the same orthophotomsaic. The original images were acquired by a DJI Phantom 4 Pro V2.0 flying at a height of around 3m above ground. These were acquired as part of the survey of the city of Abdera and its environments. These images correspond to plot 591. These are preliminary results of work in progress. Future research will provide more developed algorithms and processes. This is only intended to document the provisional results as they were in early summer 2020.