Daniele Ehrlich

A Global Human Settlement Layer From Optical HR/VHR RS Data: Concept and First Results

A general framework for processing high and very-high resolution imagery in support of a Global Human Settlement Layer (GHSL) is presented together with a discussion on the results of the first operational test of the production workflow. The test involved the mapping of 24.3 million km2 of the Earth surface spread in four continents, corresponding to an estimated population of 1.3 billion people in 2010. The resolution of the input image data ranges from 0.5 to 10 meters, collected by a heterogeneous set of platforms including satellite SPOT (2 and 5), CBERS 2B, RapidEye (2 and 4), WorldView (1 and 2), GeoEye 1, QuickBird 2, Ikonos 2, and airborne sensors. Several imaging modes were tested including panchromatic, multispectral and pan-sharpened images. A new fully automatic image information extraction, generalization and mosaic workflow is presented that is based on multiscale textural and morphological image features extraction. New image feature compression and optimization are introduced, together with new learning and classification techniques allowing for the processing of HR/VHR image data using low-resolution thematic layers as reference. A new systematic approach for quality control and validation allowing global spatial and thematic consistency checking is proposed and applied. The quality of the results are discussed by sensor, band, resolution, and eco-regions. Critical points, lessons learned and next steps are highlighted.

Information extraction from very high resolution satellite imagery over Lukole refugee camp, Tanzania

This paper addresses information extraction from IKONOS imagery over the Lukole refugee camp in Tanzania. More specific, it describes automatic image analysis procedures for a rapid and reliable identification of refugee tents as well as their spatial extent. From the identified tents, the number of refugees can be derived and a map of the camp can be generated, which can be used for improving refugee camp management. Four information extraction methods have been tested and compared: supervised classification, unsupervised classification, multi-resolution segmentation and mathematical morphology analysis. The latter two procedures based on object-oriented classifiers perform best with a spatial accuracy above 85% and a statistical accuracy above 97%. These methods could be used for refugee camp information extraction in other geographical settings and on imagery with different spatial and spectral resolutions.

Identifying damage caused by the 2008 Wenchuan earthquake from VHR remote sensing data

Abstract The paper discusses the potential of very high resolution (VHR) satellite imagery for post-earthquake damage assessment in comparison with the role of aerial photographs. Post-disaster optical and radar satellite data are assessed for their ability to resolve collapsed buildings, destroyed transportation infrastructure, and specific land cover changes. Optical VHR imagery has shown to be effective in quantifying building stock and for assessing damage at the building level. High-resolution synthetic aperture radar (SAR) imagery requires further research to identify optimum information extraction procedures for rapid assessment of affected buildings. Based on current technical and operational capabilities increasing efforts should be devoted to the generation of spatial datasets for disaster preparedness.

Toward Global Automatic Built-Up Area Recognition Using Optical VHR Imagery

The work presented here tests an automatic procedure able to recognize the presence of built-up areas in the satellite images with the output nominal scale of 1:50,000. The input data is a set of 54 Ikonos and Quick Bird scenes considered as representative of the variety of human settlement patterns in large cities at global level. The methodology for automatic image information extraction is based on calculation of anisotropic rotation-invariant textural grey-level co-occurrence measures, also called PANTEX methodology. The total area analyzed covers 35,000 km2. The data under test shows high variety in latitude, season, sun elevation and sun azimuth at the time of image data collection. The output of the automatic image information retrieval is evaluated by comparison with a collection of reference information visually interpreted from the same satellite data input. Two complementary evaluation strategies are presented here: i) interactive selection of one threshold level in the textural measurement and then unsupervised application of the same threshold level to all the datasets under test, and ii) per-scene optimization of the threshold based on the available reference samples. This work briefly summarizes the nature of the errors and implications for global settlement classification.

Performance measures for object detection evaluation

We propose a new procedure for quantitative evaluation of object detection algorithms. The procedure consists of a matching stage for finding correspondences between reference and output objects, an accuracy score that is sensitive to object shapes as well as boundary and fragmentation errors, and a ranking step for final ordering of the algorithms using multiple performance indicators. The procedure is illustrated on a building detection task where the resulting rankings are consistent with the visual inspection of the detection maps.

Extracting building stock information from optical satellite imagery for mapping earthquake exposure and its vulnerability

The paper focuses on automatic extraction of building stock information for quantifying physical exposure and its vulnerability from High and Very High Resolution (VHR) optical satellite imagery. We use two case studies. In Sana’a (Yemen), we use automatic techniques to extract the building stock as well as building height that is used to characterize its vulnerability. In Port-au-Prince (Haiti)—the area affected by the 2010 earthquake—we map the building stock based on a pre-disaster imagery, and we show the added value of area-based information when added to point-based damage assessment from visual change interpretation of post-disaster aerial images. This paper shows that VHR imagery can be used to locate and quantify the building stock and its height. This paper also shows that damages measured from changes detected from pre- and post-disaster imagery can in principle map and provide vulnerability information related to the structural fragility of the building stock.

Settlement location and population density estimation in rugged terrain using information derived from Landsat ETM and SRTM data

It is useful to have a disaggregated population database at uniform grid units in disaster situations. This study presents a method for settlement location probability and population density estimations at a 90 m resolution for northern Iraq using the Shuttle Radar Topographic Mission (SRTM) digital terrain model and Landsat Enhanced Thematic Mapper satellite imagery. A spatial model each for calculating the probability of settlement location and for estimating population density is described. A randomly selected subset of field data (equivalent to 50%) is first analysed for statistical links between settlement location probability and population density; and various biophysical features which are extracted from Landsat or SRTM data. The model is calibrated using this subset. Settlement location probability is attributed to the distance from roads and water bodies and land cover. Population density can be estimated based upon land cover and topographic features. The Landsat data are processed using a segmentation and subsequent feature–based classification approach making this method robust to seasonal variations in imagery and therefore applicable to a time series of images regardless of acquisition date. The second half of the field data is used to validate the model. Results show a reasonable estimate of population numbers (r = 0.205, p<0.001) for both rural and urban settlements. Although there is a strong overall correlation between the results of this and the LandScan model (r = 0.464, p<0.001), this method performs better than the 1 km resolution LandScan grid for settlements with fewer than 1000 people, but is less accurate for estimating population numbers in urban areas (LandScan rural r = 0.181, p<0.001; LandScan urban r = 0.303, p<0.001). The correlation between true urban population numbers is superior to that of LandScan however when the 90 m grid values are summed using a filter which corresponds to the LandScan spatial resolution (r = 0.318, p<0.001).

Quantifying the building stock from optical high-resolution satellite imagery for assessing disaster risk

This study uses high-resolution (HR) satellite imagery to quantify the stock of buildings, referred herein as building stock. The risk assessment requires information on the natural hazards and on the element at risk, that is the building stock in this article. This study combines (1) texture-based image processing to map built-up areas, (2) statistical sampling that allows locating the building samples and (3) photo-interpretation to encoding building footprints. Statistical inference is then used to quantify the building stock per class of building size. Legaspi in the Philippines is used as a case study. The results show that texture-based computer algorithms provide accurate area estimations of the built-up, that the detail of HR imagery allows the mapping of single buildings using photo-interpretation, and that a systematic sampling approach that uses building encoding and built-up maps can be used to quantify the building stock.

Development of an object-oriented classification model using very high resolution satellite imagery for monitoring diamond mining activity

Very high resolution (VHR) Ikonos images were analysed to assess the state of activity of a diamond mine extraction site in Africa. The methodology uses a dynamic approach, based on an object‐oriented classification of two sets of satellite remote sensing data that were acquired four months apart. The data was processed according to a supervised maximum likelihood classification system, using fuzzy sets of membership functions. Additionally, field information gathered from experts in the sector of diamond mining was used. The results confirm the usefulness of VHR satellite imagery for the identification of both artisanal and industrial diamond mining. The bi‐temporal dataset allows information on the evolution of the activity to be derived during a first period.

Pan-European seismic risk assessment: a proof of concept using the Earthquake Loss Estimation Routine (ELER)

One of the key objectives of the new EU civil protection mechanism is an enhanced understanding of risks the EU is facing. Developing a European perspective may create significant opportunities of successfully combining resources for the common objective of preventing and mitigating shared risks. Risk assessments and mapping represent the first step in these preventive efforts. The EU is facing an increasing number of natural disasters. Among them earthquakes are the second deadliest after extreme temperatures. A better-shared understanding of where seismic risk lies in the EU is useful to identify which regions are most at risk and where more detailed seismic risk assessments are needed. In that scope, seismic risk assessment models at a pan-European level have a great potential in obtaining an overview of the expected human and economic losses using a homogeneous quantitative approach and harmonized datasets. This study strives to demonstrate the feasibility of performing a seismic risk assessment at a pan-European level with an open access methodology and using open datasets available across the EU. It also aims at highlighting the challenges and needs in datasets and the information gaps for a consistent assessment of seismic risk at the pan-European level. Results are expressed as expected casualties and economic losses for a return period of 475-year. The study constitutes a “proof of concept” that can complement the information provided by Member States in their National Risk Assessments. Its main contribution lies in pooling open-access data from different sources in a homogeneous format, which could serve as baseline data for performing more in depth risk assessments in Europe.