scholarly journals Detection of Archaeological Surface Ceramics Using Deep Learning Image-Based Methods and Very High-Resolution UAV Imageries

Land ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1365
Author(s):  
Athos Agapiou ◽  
Athanasios Vionis ◽  
Giorgos Papantoniou
Keyword(s):  
Image Processing ◽  
High Resolution ◽  
Near Infrared ◽  
Google Earth ◽  
Single Shot ◽  
Archaeological Survey ◽  
Cultural Biography ◽  
First Case ◽  
Surface Survey ◽  
Very High

Mapping surface ceramics through systematic pedestrian archaeological survey is considered a consistent method to recover the cultural biography of sites within a micro-region. Archaeologists nowadays conduct surface survey equipped with navigation devices counting, documenting, and collecting surface archaeological potsherds within a set of plotted grids. Recent advancements in unmanned aerial vehicles (UAVs) and image processing analysis can be utilised to support such surface archaeological investigations. In this study, we have implemented two different artificial intelligence image processing methods over two areas of interest near the present-day village of Kophinou in Cyprus, in the Xeros River valley. We have applied a random forest classifier through the Google Earth Engine big data cloud platform and a Single Shot Detector neural network in the ArcGIS Pro environment. For the first case study, the detection was based on red–green–blue (RGB) high-resolution orthophotos. In contrast, a multispectral camera covering both the visible and the near-infrared parts of the spectrum was used in the second area of investigation. The overall results indicate that such an approach can be used in the future as part of ongoing archaeological pedestrian surveys to detect scattered potsherds in areas of archaeological interest, even if pottery shares a very high spectral similarity with the surface.

scholarly journals Potentialities and Limitations of Research on VHRS Data: Alexander the Great’s Military Camp at Gaugamela on the Navkur Plain in Kurdish Iraq as a Test Case

2021 ◽  
Vol 13 (5) ◽  
pp. 904
Author(s):  
Tomasz Pirowski ◽  
Michał Marciak ◽  
Marcin Sobiech
Keyword(s):  
Image Processing ◽  
High Resolution ◽  
Vegetation Indices ◽  
Test Case ◽  
Linear Features ◽  
Negative Results ◽  
Research Perspectives ◽  
Military Camp ◽  
Different Color ◽  
Very High

This paper presents a selected aspect of research conducted within the Gaugamela Project, which seeks to finally identify the location of one of the most important ancient battles: the Battle of Gaugamela (331 BCE). The aim of this study was to discover material remains of the Macedonian military camp on the Navkur Plain in Kurdish Iraq. For this purpose, three very high resolution satellite (VHRS) datasets from Pleiades and WorldView-2 were acquired and subjected to multi-variant image processing (development of different color composites, integration of multispectral and panchromatic images, use of principle component analysis transformation, use of vegetation indices). Documentation of photointerpretation was carried out through the vectorization of features/areas. Due to the character of the sought-after artifacts (remnants of a large enclosure), features were categorized into two types: linear features and areal features. As a result, 19 linear features and 2 areal features were found in the study area of the Mahad hills. However, only a few features fulfilled the expected geometric criteria (layout and size) and were subjected to field groundtruthing, which ended in negative results. It is concluded that no traces have been found that could be interpreted as remnants of an earthen enclosure capable of accommodating around 47,000 soldiers. Further research perspectives are also suggested.

scholarly journals Characterizing the Spatial and Temporal Availability of Very High Resolution Satellite Imagery in Google Earth and Microsoft Bing Maps as a Source of Reference Data

Land ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 118 ◽  
Author(s):  
Myroslava Lesiv ◽  
Linda See ◽  
Juan Laso Bayas ◽  
Tobias Sturn ◽  
Dmitry Schepaschenko ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Reference Data ◽  
Temporal Distribution ◽  
Google Earth ◽  
Training Data ◽  
Visual Interpretation ◽  
The Usa ◽  
Very High

Very high resolution (VHR) satellite imagery from Google Earth and Microsoft Bing Maps is increasingly being used in a variety of applications from computer sciences to arts and humanities. In the field of remote sensing, one use of this imagery is to create reference data sets through visual interpretation, e.g., to complement existing training data or to aid in the validation of land-cover products. Through new applications such as Collect Earth, this imagery is also being used for monitoring purposes in the form of statistical surveys obtained through visual interpretation. However, little is known about where VHR satellite imagery exists globally or the dates of the imagery. Here we present a global overview of the spatial and temporal distribution of VHR satellite imagery in Google Earth and Microsoft Bing Maps. The results show an uneven availability globally, with biases in certain areas such as the USA, Europe and India, and with clear discontinuities at political borders. We also show that the availability of VHR imagery is currently not adequate for monitoring protected areas and deforestation, but is better suited for monitoring changes in cropland or urban areas using visual interpretation.

Collecting training data to map forest management at global scale

2021 ◽  
Author(s):  
Myroslava Lesiv ◽  
Dmitry Schepaschenko ◽  
Martina Dürauer ◽  
Marcel Buchhorn ◽  
Ivelina Georgieva ◽  
...  
Keyword(s):  
Forest Management ◽  
High Resolution ◽  
Remotely Sensed ◽  
Global Scale ◽  
Google Earth ◽  
Training Data ◽  
Tree Cover ◽  
Data Set ◽  
The World ◽  
Very High

<p>Spatially explicit information on forest management at a global scale is critical for understanding the current status of forests for sustainable forest management and restoration. Whereas remotely sensed based datasets, developed by applying ML and AI algorithms, can successfully depict tree cover and other land cover types, it has not yet been used to depict untouched forest and different degrees of forest management. We show for the first time that with sufficient training data derived from very high-resolution imagery a differentiation within the tree cover class of various levels of forest management is possible.</p><p>In this session, we would like to present our approach for labeling forest related training data by using Geo-Wiki application (https://www.geo-wiki.org/). Moreover, we would like to share a new open global training data set on forest management we collected from a series of Geo-Wiki campaigns. In February 2019, we organized an expert workshop to (1) discuss the variety of forest management practices that take place in different parts of the world; (2) generalize the definitions for the application at global scale; (3) finalize the Geo-Wiki interface for the crowdsourcing campaigns; and (4) build a data set of control points (or the expert data set), which we used later to monitor the quality of the crowdsourced contributions by the volunteers. We involved forest experts from different regions around the world to explore what types of forest management information could be collected from visual interpretation of very high-resolution images from Google Maps and Microsoft Bing, in combination with Sentinel time series and Normalized Difference Vegetation Index (NDVI) profiles derived from Google Earth Engine (GEE). Based on the results of this analysis, we expanded these campaigns by involving a broader group of participants, mainly people recruited from remote sensing, geography and forest research institutes and universities.</p><p>In total, we collected forest data for approximately 230 000 locations globally. These data are of sufficient density and quality and therefore could be used in many ML and AI applications for forests at regional and local scale.  We also provide an example of ML application, a remotely sensed based global forest management map at a 100 m resolution (PROBA-V) for the year 2015. It includes such classes as intact forests, forests with signs of human impact, including clear cuts and logging, replanted forest, woody plantations with a rotation period up to 15 years, oil palms and agroforestry. The results of independent statistical validation show that the map’s overall accuracy is 81%.</p>

scholarly journals Detection of Archaeological Looting from Space: Methods, Achievements and Challenges

2019 ◽  
Vol 11 (20) ◽  
pp. 2389 ◽  
Author(s):  
Deodato Tapete ◽  
Francesca Cigna
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
High Resolution ◽  
Satellite Data ◽  
Satellite Images ◽  
Google Earth ◽  
Optical Data ◽  
Mena Region ◽  
Processing Methods ◽  
Optical Imagery

Illegal excavations in archaeological heritage sites (namely “looting”) are a global phenomenon. Satellite images are nowadays massively used by archaeologists to systematically document sites affected by looting. In parallel, remote sensing scientists are increasingly developing processing methods with a certain degree of automation to quantify looting using satellite imagery. To capture the state-of-the-art of this growing field of remote sensing, in this work 47 peer-reviewed research publications and grey literature are reviewed, accounting for: (i) the type of satellite data used, i.e., optical and synthetic aperture radar (SAR); (ii) properties of looting features utilized as proxies for damage assessment (e.g., shape, morphology, spectral signature); (iii) image processing workflows; and (iv) rationale for validation. Several scholars studied looting even prior to the conflicts recently affecting the Middle East and North Africa (MENA) region. Regardless of the method used for looting feature identification (either visual/manual, or with the aid of image processing), they preferred very high resolution (VHR) optical imagery, mainly black-and-white panchromatic, or pansharpened multispectral, whereas SAR is being used more recently by specialist image analysts only. Yet the full potential of VHR and high resolution (HR) multispectral information in optical imagery is to be exploited, with limited research studies testing spectral indices. To fill this gap, a range of looted sites across the MENA region are presented in this work, i.e., Lisht, Dashur, and Abusir el Malik (Egypt), and Tell Qarqur, Tell Jifar, Sergiopolis, Apamea, Dura Europos, and Tell Hizareen (Syria). The aim is to highlight: (i) the complementarity of HR multispectral data and VHR SAR with VHR optical imagery, (ii) usefulness of spectral profiles in the visible and near-infrared bands, and (iii) applicability of methods for multi-temporal change detection. Satellite data used for the demonstration include: HR multispectral imagery from the Copernicus Sentinel-2 constellation, VHR X-band SAR data from the COSMO-SkyMed mission, VHR panchromatic and multispectral WorldView-2 imagery, and further VHR optical data acquired by GeoEye-1, IKONOS-2, QuickBird-2, and WorldView-3, available through Google Earth. Commonalities between the different image processing methods are examined, alongside a critical discussion about automation in looting assessment, current lack of common practices in image processing, achievements in managing the uncertainty in looting feature interpretation, and current needs for more dissemination and user uptake. Directions toward sharing and harmonization of methodologies are outlined, and some proposals are made with regard to the aspects that the community working with satellite images should consider, in order to define best practices of satellite-based looting assessment.

Operational calibration of the Advanced Very High Resolution Radiometer (AVHRR) visible and near-infrared channels

2010 ◽  
Vol 36 (5) ◽  
pp. 602-616 ◽  
Author(s):  
Xiangqian Wu ◽  
Jerry T. Sullivan ◽  
Andrew K. Heidinger
Keyword(s):  
High Resolution ◽  
Near Infrared ◽  
Very High

scholarly journals A Mixed Property-Based Automatic Shadow Detection Approach for VHR Multispectral Remote Sensing Images

2018 ◽  
Vol 8 (10) ◽  
pp. 1883 ◽  
Author(s):  
Hongyin Han ◽  
Chengshan Han ◽  
Xucheng Xue ◽  
Changhong Hu ◽  
Liang Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Near Infrared ◽  
Shadow Detection ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Multispectral Remote Sensing ◽  
The Difference ◽  
Mixed Property ◽  
Very High

Shadows in very high-resolution multispectral remote sensing images hinder many applications, such as change detection, target recognition, and image classification. Though a wide variety of significant research has explored shadow detection, shadow pixels are still more or less omitted and are wrongly confused with vegetation pixels in some cases. In this study, to further manage the problems of shadow omission and vegetation misclassification, a mixed property-based shadow index is developed for detecting shadows in very high-resolution multispectral remote sensing images based on the difference of the hue component and the intensity component between shadows and nonshadows, and the difference of the reflectivity of the red band and the near infrared band between shadows and vegetation cover in nonshadows. Then, the final shadow mask is achieved, with an optimal threshold automatically obtained from the index image histogram. To validate the effectiveness of our approach for shadow detection, three test images are selected from the multispectral WorldView-3 images of Rio de Janeiro, Brazil, and are tested with our method. When compared with other investigated standard shadow detection methods, the resulting images produced by our method deliver a higher average overall accuracy (95.02%) and a better visual sense. The highly accurate data show the efficacy and stability of the proposed approach in appropriately detecting shadows and correctly classifying shadow pixels against the vegetation pixels for very high-resolution multispectral remote sensing images.

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