Visualization products computed from a raster elevation model still form the basis of most archaeological and geomorphological enquiries of lidar data. We believe there is a need to improve the ...existing visualizations and create meaningful image combinations that preserve positive characteristics of individual techniques. In this paper, we list the criteria a good visualization should meet, present five different blend modes (normal, screen, multiply, overlay, luminosity), which combine various images into one, discuss their characteristics, and examine how they can be used to improve the visibility (recognition) of small topographical features. Blending different relief visualization techniques allows for a simultaneous display of distinct topographical features in a single (enhanced) image. We provide a “recipe” and a tool for a mix of visualization techniques and blend modes, including all the settings, to compute a visualization for archaeological topography that meets all of the criteria of a good visualization.
Archaeologists engaging with Airborne Laser Scanning (ALS) data rely heavily on manual inspection of various derived visualizations. However, manual inspection of ALS data is extremely time-consuming ...and as such presents a major bottleneck in the data analysis workflow. We have therefore set out to learn and test a deep neural network model for classifying from previously manually annotated ancient Maya structures of the Chactún archaeological site in Campeche, Mexico. We considered several variations of the VGG-19 Convolutional Neural Network (CNN) to solve the task of classifying visualized example structures from previously manually annotated ALS images of man-made aguadas, buildings and platforms, as well as images of surrounding terrain (four classes and over 12,000 anthropogenic structures). We investigated how various parameters impact model performance, using: (a) six different visualization blends, (b) two different edge buffer sizes, (c) additional data augmentation and (d) architectures with different numbers of untrainable, frozen layers at the beginning of the network. Many of the models learned under the different scenarios exceeded the overall classification accuracy of 95%. Using overall accuracy, terrain precision and recall (detection rate) per class of anthropogenic structure as criteria, we selected visualization with slope, sky-view factor and positive openness in separate bands; image samples with a two-pixels edge buffer; Keras data augmentation; and five frozen layers as the optimal combination of building blocks for learning our CNN model.
Remote sensing has become the most important data source for the digital elevation model (DEM) generation. DEM analyses can be applied in various fields and many of them require appropriate DEM ...visualization support. Analytical hill-shading is the most frequently used relief visualization technique. Although widely accepted, this method has two major drawbacks: identifying details in deep shades and inability to properly represent linear features lying parallel to the light beam. Several authors have tried to overcome these limitations by changing the position of the light source or by filtering. This paper proposes a new relief visualization technique based on diffuse, rather than direct, illumination. It utilizes the sky-view factor—a parameter corresponding to the portion of visible sky limited by relief. Sky-view factor can be used as a general relief visualization technique to show relief characteristics. In particular, we show that this visualization is a very useful tool in archaeology as it improves the recognition of small scale features from high resolution DEMs.
Hillshaded digital elevation models are a well-known information layer used to determine the geomorphological properties of landslides. However, their use is limited because the results are dependent ...on a particular sun azimuth and elevation. Approaches proposed to overcome this bias include positive openness, sky-view factor, red relief image maps, and prismatic openness. We propose an upgrade to all these methods, a method named Visualization for Archaeological Topography (VAT). The method is based on a fusion of four information layers into a single image (hillshaded terrain, slope, positive openness, and sky-view factor). VAT can be used to enhance visibility of features of varied scale, height, orientation, and form that sit on terrain ranging from extremely flat to very steep. Besides this, the merits of VAT are that the results are comparable across diverse geographical areas. We have successfully tested the method for landslide recognition and analysis in five different areas in the Vipava Valley (SW Slovenia). Geomorphology of the area is very diverse and holds various types of mass movements. In contrast to classical hillshaded digital elevation models (DEMs), the geomorphological features of landslides obtained by the VAT method are very clearly seen in all studied mass movements.
This paper presents visualisation techniques of high-resolution digital elevation models (DEMs) for visual detection of archaeological features. The methods commonly used in archaeology are reviewed ...and improvements are suggested. One straightforward technique that has so far not been used in archaeology – the shift method – is presented. The main purpose of this article is to compare and evaluate different visualisation methods. Two conclusions have been reached. Where a single method must be chosen – for printing or producing digital images for non-professionals – the use of sky view factor or slope gradient is endorsed, both presented in greyscale. Otherwise interpreters should choose different techniques on different terrain types: shift on flat terrain, sky view factor on mixed terrain, slope gradient on sloped terrain and sky view factor (preferably as a composite image with slope gradient) on rugged terrain.
► Comparison of visualisation techniques of lidar-derived DEMs for archaeology. ► We find that there is no single best method. ► We conclude that different methods must be chosen on different terrain types. ► As a single overall method the use of sky view factor or slope gradient is endorsed.
•GEDI is a lidar instrument mounted on the International Space Station.•We examined the use of its data for archaeological feature recognition.•Low density makes the dataset as yet unsuitable for the ...intended application.
Airborne laser scanning (ALS) greatly accelerated and expanded traditional archaeological landscape surveys in the forested regions of the ancient Maya. Private and public funding enabled landscape visualizations ranging from site-scale to almost regional investigations. However large-scale, the airborne scanning missions are limited in the area they can cover. This is why in this paper we analyze the potential of a free, globally available lidar dataset for archaeological exploration in a forested environment. The Global Ecosystem Dynamics Investigation (GEDI) is a full waveform lidar instrument mounted on the International Space Station. The study area is Chactún, one of the largest Maya urban centers in the central lowlands of the Yucatan peninsula. Compared to airborne laser systems, the scanning density of GEDI is low; therefore, we examined whether the density and scanning pattern enable direct observation of buildings from a derived elevation model, and whether the presence of buildings can be assumed from the statistics of the canopy structure. Our research shows that the lack of coverage and low density of GEDI points makes the dataset as yet unsuitable for the intended application of archaeological feature recognition. The presence of buildings in our study area can neither be directly observed nor assumed based on biophysical parameters.
The Pannonian Basin in southeastern Europe is heavily used for rain-fed agriculture. The region experienced several droughts in the last years, causing major yield losses. Ongoing climate change, ...characterised by increasing temperatures and potential evapotranspiration, and by changes in precipitation distribution will likely increase the frequency and intensity of drought episodes in the future. Hence, ongoing monitoring of droughts and estimation of their impact on agriculture is necessary to adapt agricultural practices to changing weather and climate extremes. Several regional initiatives, projects and online tools have been established to facilitate drought monitoring and management in the Pannonian Basin. However, reliable systems to forecast potential drought impacts on plant productivity and agricultural yields at monthly to seasonal scales are only in their infancy, as plant response to climatic extremes is still poorly understood. With the increasing availability of high-resolution and long-term Earth Observation (EO) data and recent progress in machine learning and artificial intelligence, further improvements in drought monitoring and impact prediction capacities are expected. Here we review the current state of drought monitoring in the Pannonian Basin, identify EO-based variables to potentially improve regional drought impact monitoring and outline future perspectives for seasonal forecasts of drought impacts on agriculture.
Until recently, an extensive area in the central lowlands of the Yucatán peninsula was completely unexplored archaeologically. In 2013 and 2014, during initial surveys in the northern part of the ...uninhabited Calakmul Biosphere Reserve in eastern Campeche, Mexico, we located Chactún, Tamchén and Lagunita, three major Maya centers with some unexpected characteristics. Lidar data, acquired in 2016 for a larger area of 240 km2, revealed a thoroughly modified and undisturbed archaeological landscape with a remarkably large number of residential clusters and widespread modifications related to water management and agriculture. Substantial additional information was obtained through field surveys and test excavations in 2017 and 2018. While hydraulic and agricultural features and their potential for solving various archaeologically relevant questions were discussed in a previous publication, here we examine the characteristics of settlement patterns, architectural remains, sculpted monuments, and ceramic evidence. The early Middle Preclassic (early first millennium BCE) material collected in stratigraphic pits at Tamchén and another locale constitutes the earliest evidence of colonization known so far in a broader central lowland area. From then until the Late Classic period, which was followed by a dramatic demographic decline, the area under study witnessed relatively constant population growth and interacted with different parts of the Maya Lowlands. However, a number of specific and previously unknown cultural traits attest to a rather distinctive regional development, providing novel information about the extent of regional variation within the Maya culture. By analyzing settlement pattern characteristics, inscriptional data, the distribution of architectural volumes and some other features of the currently visible archaeological landscape, which largely reflects the Late Classic situation, we reconstruct several aspects of sociopolitical and territorial organization in that period, highlighting similarities with and differences from what has been evidenced in the neighboring Río Bec region and elsewhere in the Maya area.
Abstract
In our study, we set out to collect a multimodal annotated dataset for remote sensing of Maya archaeology, that is suitable for deep learning. The dataset covers the area around Chactún, one ...of the largest ancient Maya urban centres in the central Yucatán Peninsula. The dataset includes five types of data records: raster visualisations and canopy height model from airborne laser scanning (ALS) data, Sentinel-1 and Sentinel-2 satellite data, and manual data annotations. The manual annotations (used as binary masks) represent three different types of ancient Maya structures (class labels: buildings, platforms, and aguadas – artificial reservoirs) within the study area, their exact locations, and boundaries. The dataset is ready for use with machine learning, including convolutional neural networks (CNNs) for object recognition, object localization (detection), and semantic segmentation. We would like to provide this dataset to help more research teams develop their own computer vision models for investigations of Maya archaeology or improve existing ones.
The paper presents a method for mapping fluvial gravel bars based on Sentinel-2 and Landsat imagery. The proposed method therefore uses spectral signal mixture analysis (SSMA) because its results ...allow the development of land cover fraction maps for surface water, gravel, and vegetation. The method is validated on a spatially heterogeneous mountainous area in the upper Soča river basin in north-west Slovenia, Central Europe. Unmixing results in highly accurate fraction maps with MAE of around 0.1. Gravel fractions are mapped the most accurately, indicating that the approach can be used successfully for fluvial gravel bar mapping. Endmember sets selected automatically perform slightly worse (MAE higher by at most 0.05) than sets selected manually based on high resolution reference data. Both Sentinel-2 and Landsat imagery can be used for accurate mapping with differences between the two remote sensing systems within 0.05 MAE. For the study area, the SSMA-based soft classification method is more accurate for land cover mapping than a Spectral Angle Mapping-based hard classification. The method is promising for an effective use in other cases where highly accurate subpixel information is needed, because it is able to detect small-scale changes that could go unnoticed with hard classification mapping.
PDF
