Ejaz Hussain

Building population mapping with aerial imagery and GIS data

Geospatial distribution of population at a scale of individual buildings is needed for analysis of peoples interaction with their local socio-economic and physical environments. High resolution aerial images are capable of capturing urban complexities and considered as a potential source for mapping urban features at this fine scale. This paper studies population mapping for individual buildings by using aerial imagery and other geographic data. Building footprints and heights are first determined from aerial images, digital terrain and surface models. City zoning maps allow the classification of the buildings as residential and non-residential. The use of additional ancillary geographic data further filters residential utility buildings out of the residential area and identifies houses and apartments. In the final step, census block population, which is publicly available from the U.S. Census, is disaggregated and mapped to individual residential buildings. This paper proposes a modified building population mapping model that takes into account the effects of different types of residential buildings. Detailed steps are described that lead to the identification of residential buildings from imagery and other GIS data layers. Estimated building populations are evaluated per census block with reference to the known census records. This paper presents and evaluates the results of building population mapping in areas of West Lafayette, Lafayette, and Wea Township, all in the state of Indiana, USA.

Building Extraction and Rubble Mapping for City Port-au-Prince Post-2010 Earthquake with GeoEye-1 Imagery and Lidar Data

0099-1112/11/7710–1011/

Object-based urban land cover classification using rule inheritance over very high-resolution multisensor and multitemporal data

3.00/0

Species Distribution Modelling of Aedes aegypti in two dengue‐endemic regions of Pakistan

Very high spatial and temporal resolution remote sensing data facilitate mapping highly complex and diverse urban environments. This study analyzed and demonstrated the usefulness of combined high-resolution aerial digital images and elevation data, and its processing using object-based image analysis for mapping urban land covers and quantifying buildings. It is observed that mapping heterogeneous features across large urban areas is time consuming and challenging. This study presents and demonstrates an approach for formulating an optimal land cover classification rule set over small representative training urban area image, and its subsequent transfer to the multisensor, multitemporal images. The classification results over the training area showed an overall accuracy of 96%, and the application of rule set to different sensor images of other test areas resulted in reduced accuracies of 91% for the same sensor, 90% and 86% for the different sensors temporal data. The comparison of reference and classified buildings showed ±4% detection errors. Classification through a transferred rule set reduced the classification accuracy by about 5%–10%. However, the trade-off for this accuracy drop was about a 75% reduction in processing time for performing classification in the training area. The factors influencing the classification accuracies were mainly the shadow and temporal changes in the class characteristics.

Performance evaluation of building detection and digital surface model extraction algorithms: Outcomes of the PRRS 2008 Algorithm Performance Contest

Statistical tools are effectively used to determine the distribution of mosquitoes and to make ecological inferences about the vector‐borne disease dynamics. In this study, we utilised species distribution models to understand spatial patterns of Aedes aegypti in two dengue‐prevalent regions of Pakistan, Lahore and Swat. Species distribution models can potentially indicate the probability of suitability of Ae. aegypti once introduced to new regions like Swat, where invasion of this species is a recent phenomenon.

Flood Mapping and Damage Assessment – A Case Study in the State of Indiana

This paper presents the initial results of the algorithm performance contest that was organized as part of the 5th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS 2008). The focus of the 2008 contest was automatic building detection and digital surface model (DSM) extraction. A QuickBird data set with manual ground truth was used for building detection evaluation, and a stereo Ikonos data set with a highly accurate reference DSM was used for DSM extraction evaluation. Nine submissions were received for the building detection task, and three submissions were received for the DSM extraction task. We provide an overview of the data sets, the summaries of the methods used for the submissions, the details of the evaluation criteria, and the results of the initial evaluation.

Urban building extraction through object-based image classification assisted by digital surface model and zoning map

Flood mapping, damage assessment, and disaster remediation involve activities and efforts from a number of governmental agencies. Under the National Flood Insurance Act 1968, the Federal Emergency Management Agency (FEMA) is responsible for identifying flood hazards nationwide, publishing and updating flood hazard information in support of the National Flood Insurance Program (NFIP). Over a period of two decades, FEMA has produced over 90,000 flood hazard maps covering approximately 150,000 square miles of floodplain. Recently, about 75% of the flood hazard maps inventory became over 10 years old. In 2003, a program was initiated for flood hazard map modernization including the conversion of paper maps to digital format. Since flood hazard mapping is part of the NFIP, a variety of maps indicating various degrees of insurance risk and premium rating are produced. However, the basic hazard maps, indicating the 1 in 100 years (1%) floodplain and the 1 in 500 years flood (0.2%) outlines, are normally produced based on detailed hydraulic modeling of river reaches at the community scale. All flood maps are made available to the public through the FEMA Map Service Center. These maps can be purchased in paper or CD format and can be viewed online (http://msc.fema.gov/). Beginning on October 1, 2009, FEMA will provide only one paper flood map and the Flood Insurance Study (FIS) report to each mapped community. All other distribution of maps and Flood Insurance Study reports will be converted to digital delivery. FEMA will continue to provide free digital map products and data to federal, state, tribal, and local NFIP stakeholders. In addition to the FEMA mapping effort, which is specifically linked to the NFIP, some states have their own flood mapping programs. They produce flood “awareness” maps that simply show flood prone areas without specific depth or other flood hazard data for a particular flood event.

Comparison of Pixel-based and Object-based classification for glacier change detection

This study develops an object-based image classification methodology for urban land covers classification, using very high resolution aerial images, elevation data and city zoning maps. Logically structured classification rules based on spectral, spatial and contextual features of the segmented objects are first created and tested over a small urban area. The same rule set is then transferred and tested on two similar images covering larger urban areas. The land cover classification results through the transferability of the rule set prove the effectiveness of the methodology and produce satisfactory classification results with an overall accuracy of 91% as against 96% that was achieved over the small representative training area. The classification methodology based on the integrated use of multiple data produces satisfactory land cover classification. Its transferability considerably reduces both the processing time and the analyst’s efforts.

Future climate and cryosphere impacts on the hydrology of a scarcely gauged catchment on the Jhelum river basin, Northern Pakistan

This research paper compares the result of Object based and Pixel based classification techniques for glacier change detection on Landsat Thematic mapper (TM) and Enhanced Thematic Mapper (ETM+) imageries. The objective of this study is to see which classification method performs better for change detection in mountainous regions. Northern face of Himalayan region, The study area is undergoing climate change in the form of rapid melting of glacial ice mass, expansion of the existing lakes and creation of new lakes. This results in Glacial Lake Outburst Flood (GLOF) and breach or outburst from ice and ‘moraine dams’ causing devastating floods downstream. The global warming phenomenon worldwide has resulted in a significant decrease in glacial cover. Glaciers change monitoring and permafrost-related hazards have long been studied using remote sensing data and techniques to assess the damage. The world is facing a serious problem of handling the climate change issue and its effects on humans as well as on natural resources. Glaciers are considered as one of the best indicators of climate change [1]. Landsat TM/ETM+ images were used for glacier change monitoring of Turkeys mountains project, Mount Suphan. The results show that about ¾ of total area of suphan glacier has been lost in 23 years. Traditional image classification methods use only the spectral information at pixel level without considering the shape of underlying objects [2]. However, object-based image classification process uses spectral and spatial dimensions (shape of feature) in order to perform classification. In this study, multi temporal Landsat TM and ETM+ image from 1990 to 2010 have been used. Initially, the traditional pixel-based classification was performed on Landsat thematic layers and layers developed from indices like NDVI and NDSII. Then object-based classification of these images was carried out. The comparison of the classification results (both qualitative and quantitative) show that the object-based approach gives about 10–15% higher accuracy, much better results in terms of area estimation and change detection of snow covered areas as compared to traditional pixel-based classification. The results also indicate that object based classification is more useful in mountainous regions to avoid confusion among classes produced by shadows.

3-Dimensional Indoor Positioning System based on WI-FI Received Signal Strength using Greedy Algorithm and Parallel Resilient Propagation

Streamflow projections are fundamental sources for future water resources strategic planning and management, particularly in high-altitude scarcely-gauged basins located in high mountain Asia. Therefore, quantification of the climate change impacts on major hydrological components (evapotranspiration, soil water storage, snowmelt-runoff, rainfall-runoff and streamflow) is of high importance and remains a challenge. For this purpose, we analysed general circulation models (GCMs) using a multiple bias correction approach and two different hydrological models i.e. the Hydrological Modelling System (HEC-HMS) and the Snowmelt Runoff Model (SRM), to examine the impact of climate change on the hydrological behaviour of the Jhelum River basin. Based on scrutiny, climate projections using four best fit CMIP5 GCMs (i.e. BCC-CSM1.1, INMCM4, IPSL-CM5A-LR and CMCC-CMS) were chosen by evaluating linear scaling, local intensity scaling (LOCI) and distribution mapping (DM) approaches at twenty climate stations. Subsequently, after calibration and validation of HEC-HMS and SRM at five streamflow gauging stations, the bias corrected projected climate data was integrated with HEC-HMS and SRM to simulate projected streamflow. Results demonstrate that the DM approach fitted the projections best. The climate projections exhibited maximum intra-annual rises in precipitation by 183.2 mm (12.74%) during the monsoon for RCP4.5 and a rise in Tmin (Tmax) by 4.77 °C (4.42 °C) during pre-monsoon, for RCP8.5 during 2090s. The precipitation and temperature rise is expected to expedite and increase snowmelt-runoff up to 48% and evapotranspiration and soil water storage up to 45%. The projections exhibited significant increases in streamflows by 330 m3/s (22.6%) for HEC-HMS and 449 m3/s (30.7%) for SRM during the pre-monfaf0000soon season by the 2090s under RCP8.5. Overall, our results reveal that the pre-monsoon season is potentially utmost affected under scenario-periods, and consequently, which has the potential to alter the precipitation and flow regime of the Jhelum River basin due to significant early snow- and glacier-melt.