Unmanned aerial vehicles (UAV) have gained tremendous interest as a platform for surveying and mapping over the last few years, and have opened up a new realm of opportunities for surveying, orthophoto production, 3D modelling and feature extraction. UAVs provide a viable and affordable alternative for the airborne and space borne sensors for the medium/large scale mapping. This paper argues that universities should expand their education and training programs to include UAV-based geomatics operations and application development. Based on the author’s own experience as well other cases, details are developed and presented in this paper with respect to the likely syllabi and practical assignments. Alternatives for hardware and software support will be briefly discussed.

Reliable building delineation in very high resolution (VHR) satellite imagery can be achieved by precise disparity information extracted from stereo pairs. However, off-nadir VHR images over urban areas contain many occlusions due to building leaning that creates gaps in the extracted disparity maps. The typical approach to fill these gaps is by interpolation. However, it inevitably degrades the quality of the disparity map and reduces the accuracy of building detection. Thus, this research proposes a registration-based technique for mapping the disparity of off-terrain objects to avoid the need for disparity interpolation and normalization. The generated disparity by the proposed technique is then used to support building detection in off-nadir VHR satellite images. Experiments in a high-rise building area confirmed that 75 percent of the detected building roofs overlap precisely the reference data, with almost 100 percent correct detection. These accuracies are substantially higher than those achieved by other published research.

The Grand Lake Meadows is an important part of the Saint John River wetlands that form the largest freshwater wetland habitat in the Maritimes (eastern Canada). Changes in the land cover and use around wetlands significantly impact their biotic diversity, alter the ecosystem, and affect their ability to support human needs. The goal for this paper was to undertake a detailed and spatially explicit inventory of local trends in land use and land cover changes in Grand Lake Meadows over a 20-year time period. This goal was achieved through classifying historical remotely-sensed images to map the state of land use and cover. Other available data were combined with this information to create a database that was used to investigate the causes and consequences of changes. The results demonstrated the flexibility and the effectiveness of this technology in establishing the necessary baseline and support information for sustaining the eco-services of a wetland. The study identified a 38% decrease in the wetland from 1990 to 2001, while there was 80% increase in the wetland area since then. The result will help managers to comprehend the dynamics of the changes, prompting a better management and implementation of LULC administration in the area.

Third-dimension information is of a great importance for several remote sensing applications, such as building detection. The main data-source for these applications is very high resolution (VHR) satellite images which allow detailed mapping of complex environments. Stereo VHR satellite images allow the extraction of two correlated types of third-dimension information: disparity and elevation information. While the disparity is measured directly, the elevation information is derived computationally. To measure the disparity information, two overlapped images are matched. However, for the backward and forward off-nadir VHR stereo images, building facades occlude areas and hence create many data gaps. When the disparity is required to represent only the off-terrain objects, interpolation and normalization techniques are typically used. However, in dense urban environments, these techniques destroy the quality of the generated data. Therefore, this paper proposes a registration-based technique to measure the disparity of the above-ground objects. The technique includes constructing epipolar images and registering them using common terrain- level features to allow direct disparity mapping for the off-terrain objects. After the implementation, the negative effects of occlusion in the off-nadir VHR stereo images are mitigated through direct disparity mapping of the above-ground objects and bypassing the interpolation and normalization steps.

Multi-view satellite images can form multiple stereo image pairs (MSIPs). Each pair allows generating two types of surface information: surface disparity maps (SDMs) and digital elevation models (DEMs). Both types are important for 3D assisted information extraction applications. Since multi-view images can be used to generate more accurate stereo-based DEMs than using one stereo-pair, it is more computationally cost-effective to generate enhanced SDMs from MSIPs. However, the disparity values from different stereo-pairs are unequal. Hence, redundant data cannot be generated to enhance the SDMs. Therefore, our research focuses on the generation of inter-proportional disparity maps from multi-view very high resolution satellite images. The method uses the sensor model information of the stereo images to construct multiple epipolar images in the object-space. After implementation, the disparity values of the constructed images were found to be proportional. This property allows the generation of SDMs that may replace the DEMs to some extent.