Earth Observation-Based Dwelling Detection Approaches in a Highly Complex Refugee Camp Environment - A Comparative Study

For effective management of refugee camps or camps for internally displaced persons (IDPs) relief organizations need up-to-date information on the camp situation, that can be provided by Earth observation (EO). In this study, different approaches were tested using the example of a highly complex camp site in Somalia.Si loogu sameeyo maareen rasmi ah xereyinka qaxootiga iyo barakacayaasha gudaha dalka, ururada samafalku waxay u baahanyihiin xog ama warar cusub oo ku saabsan xaaladaha xerooyinka. Haddaba daraasaadkan wuxuu si gaar ah u baarayaa xero ku taalla Soomaaliya.Per una gestione efficace dei campi profughi o campi per sfollati interni (IDPs), le organizzazioni umanitarie hanno bisogno di informazioni aggiornate sulla situazione del campo, che possono essere fornite con osservazioni della Terra dallo spazio (EO). In questo studio, diversi approcci sono stati testati partendo dal caso di un campo molto complesso in Somalia

Earth Observation for Humanitarian Operations. GI_Forum|GI_Forum 2015 – Geospatial Minds for Society|

The protraction of crises, civil wars, and conflicts cause countless humanitarian disasters, on the level of individuals, families, and society as a whole. Technological innovation, including the optimisation of information flows in crisis situations, is a critical asset in the humanitarian domain, while the ultimate benchmark of the usefulness of any new ‘gadget’ will be its effectiveness on the ground, and the very fact of whether it saves lives in the long run. Humanitarian aid organizations do play a critical role in this respect; they are the ones to adopt, test, improve, and further develop any new technology, in close collaboration with those providing it. Over recent years, projects and initiatives have been brought up, where research institutions and humanitarian actors share both technological and practical experience in mutual exchange. Satellite Earth observation (EO) and Geographical Information Systems (GIS) were recently adopted by the humanitarian action community to cope with these challenges, and to close the information gap. The special session EO4Hum focuses on the potential of EO data and technologies to support humanitarian action in crisis and disaster response. Turning satellite data into relevant geospatial information products for humanitarian actors remains a great challenge therein

Don’t See the Dwellings for the Trees: Quantifying the Effect of Tree Growth on Multi-temporal Dwelling Extraction in a Refugee Camp. GI_Forum|GI_Forum 2015 – Geospatial Minds for Society|

Automated and visual approaches for the monitoring of refugee or IDP camps based on satellite data are very important as independent information sources, especially for insecure and remote areas. Nevertheless, monitoring based on satellite data always has a certain degree of uncertainty, e.g. due to data quality, complexity of the area of investigation, seasonal pheonological problems, or algorithmic limitations. Within this paper, we aim to quantify one of these limiting aspects: the factor of vegetation (i.e. tree) growth and its effect on multi-temporal dwelling monitoring, hampering the identification of dwellings on the ground. For the refugee camp Djabal, Chad, we found that 2506 dwellings (25 %) of 2010 are at least partly affected by tree growth three years later (2013), which is influencing automated extraction methods, as well as visual interpretations. 395 of these dwellings were completely covered by vegetation and vegetation shadow, and were therefore not detectable anymore. Taking this factor into account, the decrease of dwellings between 2010 and 2013 is potentially lowered from 10 % to 5 %

Earth Observation-Based Dwelling Detection Approaches in a Highly Complex Refugee Camp Environment — A Comparative Study

For effective management of refugee camps or camps for internally displaced persons (IDPs) relief organizations need up-to-date information on the camp situation. In cases where detailed field assessments are not available, Earth observation (EO) data can provide important information to get a better overview about the general situation on the ground. In this study, different approaches for dwelling detection were tested using the example of a highly complex camp site in Somalia. On the basis of GeoEye-1 imagery, semi-automatic object-based and manual image analysis approaches were applied, compared and evaluated regarding their analysis results (absolute numbers, population estimation, spatial pattern), statistical correlations and production time. Although even the results of the visual image interpretation vary considerably between the interpreters, there is a similar pattern resulting from all methods, which shows same tendencies for dense and sparse populated areas. The statistical analyses revealed that all approaches have problems in the more complex areas, whereas there is a higher variance in manual interpretations with increasing complexity. The application of advanced rule sets in an object-based environment allowed a more consistent feature extraction in the area under investigation that can be obtained at a fraction of the time compared to visual image interpretation if large areas have to be observed

Stratified template matching to support refugee camp analysis in OBIA workflows

Accurate and reliable information about the situation in refugee or internally displaced person camps is very important for planning any kind of help like health care, infrastructure, or vaccination campaigns. The number and spatial distribution of single dwellings extracted semi-automatically from very high-resolution (VHR) satellite imagery as an indicator for population estimations can provide such important information. The accuracy of the extracted dwellings can vary quite a lot depending on various factors. To enhance established single dwelling extraction approaches, we have tested the integration of stratified template matching methods in object-based image analysis (OBIA) workflows. A template library for various dwelling types (template samples are taken from ten different sites using 16 satellite images), incorporating the shadow effect of dwellings, was established. Altogether, 18 template classes were created covering typically occurring dwellings and their cast shadows. The created template library aims to be generally applicable in similar conditions. Compared to pre-existing OBIA classifications, the approach could increase the producers accuracy by 11.7 percentage points on average and slightly increase the users accuracy. These results show that the stratified integration of template matching approaches in OBIA workflows is a possibility to further improve the results of semi-automated dwelling extraction, especially in complex situations.(VLID)191236

Object-based Image Analysis Using VHR Satellite Imagery for Monitoring the Dismantling of a Refugee Camp after a Crisis: The Case of Lukole, Tanzania. GI_Forum 2014 – Geospatial Innovation for Society|

The use of HR and VHR (high/very high spatial resolution) imagery and OBIA (objectbased image analysis) offers new possibilities for monitoring activities in and around refugee camps to manage, understand, and assess developments and impacts of the camp on its environment (see for example TIEDE et al. 2013, HAGENLOCHER et al. 2012). Here we demonstrate how VHR imagery in combination with OBIA can be used to retrieve and create valuable information about a remote refugee camp and its surroundings before, during, and after the dismantling and the repatriation process. Feature extraction approaches for single dwellings and further information retrieval, as well as land cover classification for the refugee camp Lukole in Tanzania were combined for an integrated monitoring approach

Monitoring Displaced People in Crisis Situations Using Multi-temporal VHR Satellite Data During Humanitarian Operations in South Sudan. GI_Forum|GI_Forum 2015 – Geospatial Minds for Society|

Natural disasters, changing environmental conditions, and violent regional conflicts are main drivers for population displacement. Worldwide, more than 50 million people are displaced. One tragic example of huge displacement due to a conflict situation is the Republic of South Sudan, where 1.7 million people have been forced to flee their homes since December 2013. Most of them found refuge in numerous spontaneous settlements, either camps for internally displaced people (IDPs) within the country, or refugee camps in neighbouring countries. In such crisis situations, humanitarian organisations often do not have access to the areas and only have vague information on the location and amount of affected population. Using very high resolution (VHR) satellite imagery, rumours about displaced people can be generally verified or falsified, while for areas where displaced people gather, information on amount and spatial distribution of dwellings can be extracted for population estimates. Such information assists in planning services like health care or vaccination campaigns and planning of needed infrastructure like boreholes, latrines or hospitals. Camps in the setup and construction phase are often highly dynamic and require regular monitoring. Beyond this emergency phase, specific information is also requested by organisations involved in camp management in all other phases of humanitarian crisis response, i.e. in the care and maintenance phase, as well as the repatriation phase

Monitoring Displaced People in Crisis Situations Using Multi-temporal VHR Satellite Data During Humanitarian Operations in South Sudan. GI_Forum|GI_Forum 2015 – Geospatial Minds for Society|

Natural disasters, changing environmental conditions, and violent regional conflicts are main drivers for population displacement. Worldwide, more than 50 million people are displaced. One tragic example of huge displacement due to a conflict situation is the Republic of South Sudan, where 1.7 million people have been forced to flee their homes since December 2013. Most of them found refuge in numerous spontaneous settlements, either camps for internally displaced people (IDPs) within the country, or refugee camps in neighbouring countries. In such crisis situations, humanitarian organisations often do not have access to the areas and only have vague information on the location and amount of affected population. Using very high resolution (VHR) satellite imagery, rumours about displaced people can be generally verified or falsified, while for areas where displaced people gather, information on amount and spatial distribution of dwellings can be extracted for population estimates. Such information assists in planning services like health care or vaccination campaigns and planning of needed infrastructure like boreholes, latrines or hospitals. Camps in the setup and construction phase are often highly dynamic and require regular monitoring. Beyond this emergency phase, specific information is also requested by organisations involved in camp management in all other phases of humanitarian crisis response, i.e. in the care and maintenance phase, as well as the repatriation phase

Object-based Image Analysis Using VHR Satellite Imagery for Monitoring the Dismantling of a Refugee Camp after a Crisis: The Case of Lukole, Tanzania. GI_Forum 2014 – Geospatial Innovation for Society|

The use of HR and VHR (high/very high spatial resolution) imagery and OBIA (objectbased image analysis) offers new possibilities for monitoring activities in and around refugee camps to manage, understand, and assess developments and impacts of the camp on its environment (see for example TIEDE et al. 2013, HAGENLOCHER et al. 2012). Here we demonstrate how VHR imagery in combination with OBIA can be used to retrieve and create valuable information about a remote refugee camp and its surroundings before, during, and after the dismantling and the repatriation process. Feature extraction approaches for single dwellings and further information retrieval, as well as land cover classification for the refugee camp Lukole in Tanzania were combined for an integrated monitoring approach