Confidence maps: a tool to evaluate archaeological data'srelevance in spatial analysis

Inventory data used in archaeology is often incomplete and heterogeneous. In the framework of the ArchaeDyn program, a method has been proposed to evaluate heterogeneity in archaeological inventories. The purpose of this work is to create a validation tool to interpret the results. This tool is called a “confidence map” and is produced by combining representation and reliability maps. The first step consists of generating representation maps to describe the clustering of archaeological items. The second step is based on reliability maps. Data providers are asked to define and outline the level of reliability of their data. Then the representation and reliability layers are combined using map algebra. The resulting maps allow for the comparison and analysis of data confidence

ArchaeDyn. Dynamique spatiale du peuplement et ressources naturelles : vers une analyse intégrée dans le long terme, de la Préhistoire au Moyen Âge,. ACI Espaces et territoires (contrat ET28, 2005-2007)

Rapport de fin de contrat ACIRapport de fin de contrat d'ACIProject final repor

Development of a soil indicator in eastern Languedoc (south-eastern France): a contribution of medium resolution satellite data for ancient landscape

International audienc

The within and between segment separability analysis of sealed urban land use classes

Object-based classification has proved to increase the classification capabilities of the very high resolution imagery in the last years. The classification is performed with the use of segments that represent a homogenous group of pixels. While most of the segments attributes which classification is based on is computed as a parametric summary numeric value, the emphasis of this preliminary study was to analyze whether the empirical distribution functions (ECDF) as the segments additional attribute are sufficiently representative and effective separability measure. By empirical cumulative distribution functions are meant step functions where all the pixels inside of a segment are being considered. When distribution functions are interpreted as a segments spectral signature they can be used as a graphic separability tool or as an input parameter in a classification method where segments are compared using Kolmogorov-Smirnov distance and the two-sided Kolmogorov-Smirnov test. To test the between class spectral confusion and within class variability three band color infrared orthophoto imagery with 1 m resolution for the urban areas in Ljubljana, Slovenia, was used. Typical sealed and non-vegetation land use classes such as different rooftops, roads, bare-soil, shadows and water were examined. The experimental analysis showed that Kolmogorov-Smirnov classifier has the potential to differentiate spectrally similar classes and has proven to slightly improve the number of correctly classified segments. All algorithms are implemented in the IDL programming language which will enable the design and use of graphic user interface along with the ENVIs and ENVI EXs interface and their capabilities.Pages: 2392-239

Vessel detection and classification from spaceborne optical images: A literature survey

This paper provides an overview of existing literature on vessel/ship detection and classification from optical satellite imagery. Although SAR (Synthetic Aperture Radar) is still the leading technology for maritime monitoring, the number of studies based on optical satellite data is quickly growing. Altogether we analysed 119 papers on optical vessel detection and classification for the period from 1978 to March 2017. We start by introducing all the existing sensor systems for vessel detection, but subsequently focus only on optical imaging satellites. The article demonstrates the temporal development of optical satellite characteristics and connects this to the number and frequency of publications on vessel detection. After presenting the methods used for optical imagery-based vessel detection and classification in detail, along with the achieved detection accuracies, we also report possibilities for fusing optical data with other data sources. The studied papers show that the most common factors greatly influencing the vessel detection accuracy are the following: different weather conditions affecting sea surface characteristics, the quantity of clouds and haze, solar angle, and imaging sensor characteristics. All these factors bring great variations in the selection of the most suitable method; some still continue to pose unsolved challenges. For higher relevance and wider usage, we suggest that the algorithms for detection and classification should support a variety of targets and meteorological conditions, and ideally also a variety of optical satellite sensors. At least, they should be tested on many images under different conditions. This is not usually the case in the existent literature. We also observed that many authors omit an appropriate performance quantification, which is critical for a practical assessment and a numerical comparison of the presented algorithms. Overall it can be seen that vessel monitoring from spaceborne optical images is a popular research topic and has a great operational potential in the near future due to the large amount of satellite data, much of it free and open.JRC.E.7-Knowledge for Security and Migratio