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Increasing resolution and lower cost of off‐the‐shelf digital cameras are giving rise to their use in traditional and new photogrammetric activities such as aerial mapping, transportation and surveillance as well as archaeological, industrial and medical applications. For most, if not all, photogrammetric applications, the interior orientation parameters (IOP) of the camera need to be determined and analysed. The derivation of these parameters is usually achieved through a bundle adjustment with self‐calibration procedure. Prior to using a camera in photogrammetric applications, the IOP should be estimated and their stability should be checked. Camera stability has been rarely addressed when dealing with analogue metric cameras since they have been carefully designed and built to assure the utmost stability of their internal characteristics. However, the stability of low‐cost digital cameras needs to be investigated since these cameras are not built with photogrammetric applications in mind. This paper introduces three quantitative methods for testing camera stability, where the degree of similarity between reconstructed bundles from two sets of IOP is evaluated. Each of these methods limits the position and orientation of the bundles in a different way. Hence, each method is applicable for a specific georeferencing methodology depending on similar constraints imposed by the stability measures and different georeferencing techniques. The paper will test this hypothesis on the basis of reconstruction results obtained from the use of a low‐cost digital camera in an aerial mapping project.

The steady evolution of mapping technology is leading to an increasing availability of multi‐sensory geo‐spatial datasets, such as data acquired by single‐head frame cameras, multi‐head frame cameras, line cameras, and light detection and ranging systems, at a reasonable cost. The complementary nature of the data collected by these systems makes their integration to obtain a complete description of the object space. However, such integration is only possible after accurate co‐registration of the collected data to a common reference frame. The registration can be carried out reliably through a triangulation procedure which considers the characteristics of the involved data. This paper introduces algorithms for a multi‐primitive and multi‐sensory triangulation environment, which is geared towards taking advantage of the complementary characteristics of spatial data available from the above mentioned sensors. The triangulation procedure ensures the alignment of involved data to a common reference frame. The devised methodologies are tested and proven efficient through experiments using real multi‐sensory data.

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AASHTO guidelines for the geometric design of highways require that the insides of horizontal curves be clear of sight obstructions to provide required sight distance. However, the guideline's method for determining minimum roadside clearance is suitable only for middle sections of horizontal curves at sites where the circular arcs are equal to or longer than sight distance. For other sites and sections of horizontal curves, the guideline recommends the use of graphical or computational methods. The graphical method is tedious and time-consuming, and the recommended computational method was prepared for sites with circular arcs equal to or longer than sight distance. An analytical model was developed for clearance offsets for sites with circular arcs shorter than sight distance. Offsets were defined as normal ordinates from the driver path to a curve that demarcates the roadside area that accommodates sightlines. Variables in the model are sight distance, length of transition, length of circular curve, radius of circular curve, and driver location. Design charts for maximum and intermediate offsets were developed for use by practitioners with the model. Offsets from the charts for curves that have transition arcs were significantly shorter than offsets for equivalent simple curves. With these charts, the extra excavation costs associated with the resultant bigger offsets of other models can be avoided. Study results will be of value to engineers involved in design policy, highway maintenance, and design improvement for sites with insufficient sight distance.

Registration activities combine data from different sources in order to attain higher accuracy and derive more information than available from one source. The increasing availability of a wide variety of sensors capable of capturing high quality and complementary data requires parallel efforts for developing accurate and robust registration techniques. Currently, photogrammetric and LIDAR systems are being incorporated in a wide spectrum of mapping applications such as city modeling, surface reconstruction, and object recognition. Photogrammetric processing of overlapping imagery provides accurate information regarding object space break-lines in addition to an explicit semantic description of the photographed objects. On the other hand, LIDAR systems supply dense geometric surface information in the form of non-selective points. Considering the properties of photogrammetric and LIDAR data, it is clear that the two technologies provide complementary information. However, the synergic characteristics of both systems can be fully utilized only after successful registration of the photogrammetric and LIDAR data relative to a common reference frame. The registration methodology has to deal with three issues: registration primitives, transformation function, and similarity measure. This paper presents two methodologies for utilizing straight-line features derived from both datasets as the registration primitives. The first methodology directly incorporates the LIDAR lines as control information in the photogrammetric triangulation. The second methodology starts by generating a photogrammetric model relative to an arbitrary datum. Then, LIDAR features are used as control information for the absolute orientation of the photogrammetric model. In addition to the registration methodologies, the paper presents a comparative analysis between two approaches for extracting linear features from raw and processed/interpolated LIDAR data. Also, a comparative analysis between metric analog and amateur digital cameras within the registration process will be presented. The performance analysis is based on the quality of fit of the final alignment between the LIDAR and photogrammetric models.

Diversification of water sources and water demand reduction are two vital tools in maintaining the security of urban water supplies in the United Arab Emirates (UAE). Reuse of greywater for non-potable end uses can be an effective alternative, but this resource has not yet received much attention in the UAE. Since the generation of greywater significantly differs from country to countrydepending on age, gender, habits, lifestyle, living standards and the degree of water abundance -an attempt was made to estimate internal water consumption and greywater generation in the city of Al Ain, UAE. The frequency and water requirement for personal water uses (e.g. showers, ablutions, teeth brushing, hand washing, face washing and toilet flushing) and family water uses (e.g. laundry, dish washing and house cleaning) were estimated from about 100 villa-type detached homes randomly distributed across the city. A frequency analysis was carried out using normal, lognormal, gamma and logistic distribution. The estimated average generation rate of greywater was found to be 192 litres per capita per day, which is about 69% of the average internal water consumption. The generated greywater originates from showers (49%), ablutions (18%), laundry (10%) and washbasins (23%). Based on average quantities, it was shown that the generated greywater is sufficient to fulfil the non-potable water demand in houses, but further, more rigorous, investigation is required.

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