In recent years, photogrammetry has been recognised as an extremely good surveying method when trying to produce 3D images of the Earth’s surface. This is because it can be used on demand and has the ability to create high-resolution data, including DSM layers and orthophotos (orthorectified images). Photogrammetry includes analysing Earth-based (terrestrial) data or dedicated air- and space-borne campaigns [1] [2] [3] . Photogrammetry can be used in a variety of industries, including urban mapping and planning [4] [5] , agriculture, resource management [6] [7] , recording of archaeological features [8] [9] and hydrology and hydrodynamic flood modelling [10] [11] [12] . Due to its uses, there has also been a rise in photogrammetry being used in geosciences, where it can be used for mapping, monitoring [13] [14] , and the detection of objects [15] and group changes in topography [16] .

Despite its uses, in the past, the use of aerial photogrammetry has been limited. This is because it was seen as a high-cost method of data collection and often faced difficulties when trying to collect 3D topographic data, orthophotos, topographic maps and other map features due to the large format metric cameras that were used [17] . The development of unmanned aerial vehicles (UAVs), however, has helped to make photogrammetry a more accessible means if data collection allows for the collection of images with high spatial and spectral resolutions in a way that can save both money and time. These technological advances allow for high-quality mapping of the earth’s surface using Orthoimages and also mean that 3D models (meshes) of the earth’s surface can be created with high resolution and accuracy. Alongside this, advances in computer hardware and image matching software have also meant that stereo images can be compared faster and more accurately than ever before, thus making photogrammetry a viable alternative to manned aerial photography [18] [19] [20] . In spite of these advantages though, UAVs often have weight and cost restrictions which mean that the sensors used in them are often lower quality than those that would be used during manned aerial photography. This can mean that when the sensor needs to provide accurate data in centimetres, this traditional approach may not provide suitable results unless a large number of group control points (GCPs) are distributed evenly across the sample. This can mean that a project becomes too expensive or is impractical and may even mean that inaccessible terrain gets included within the sample. In order to create overlapping imagery in a block configuration, it is important that the aerial position is precisely controlled, which can help to reduce the need for multiple GCPs [21] .

There have also been developments in Global Navigation Satellite Systems (GNSS), which can be seen to be particularly interesting in terms of this paper. There has been an increase in the use of Real-Time Kinematic (RTK) devices being placed into UAVs that are readily available. This is interesting because the use of RTKs means that the position of the UAV can be more easily tracked, and can also help to ensure that the data provided is more accurate (up to 2 cm) [22] . UAVs using this kind of technology can modulate signals between the satellites and the receivers using the GNSS carrier phase [23] . The GNSS receiver in the UAV receives a differential signal from the base station, which is corrected by the RTK and allows for a communication link. The most recent UAVs now come with RTK units onboard them and they use a dual frequency which can help to reduce atmospheric delay and provide an even more precise location. In comparison to a single frequency, the ambiguity resolution is also much quicker [24] .

Advances in remote sensing have helped to make UAVs even more useful and effective data collection tools than ever before, as it means that UAVs now have the ability to combine temporal and spatial sensing. This allows for an even more precise recognition of features, which, while positive, can also mean that the images produced are subject to noise from shadows or the salt and pepper effect [25] [26] [27] . This is due to the nature of pixels and the way in which they behave when the spatial resolution of an image is increased. Studies have shown that increasing the spatial resolution of an image can have a negative effect on data, as pixel-based techniques can make it challenging to identify features accurately [28] [29] . In order to overcome the shortfalls of pixel-based techniques, researchers have tended to move towards using object orientated classification techniques when looking at images with an extremely high spatial resolution [30] , while the use of Orthoimages in this setting is massively underused, especially in terms of mapping v features. In one study, it was found that using UAVs to recognise tree species in a mixed boreal forest gave results with an 82% accuracy rate [30] . UAVs have also been found to be very useful when mapping specific plants in open woodland. A study by Chenari et al. (2017), aimed to estimate the mean crown area of wild single level trees in open woodland and classified the Orthoimages collected using the object-oriented method. This gave a classification accuracy of 0.90 and a precision score of 0.89 [31] . UAVs can also be used to classify urban environments with increased accuracy too [32] [33] , especially when using Orthoimages and DSM, as these are useful when identifying elevated objects in urban scenes [34] [35] .

However, these building detection algorithms are not without problems though and can struggle to identify buildings when they are less than 50 km² or the building is on sloped ground. This is particularly common in casual settlements, meaning that these detection algorithms are not suitable to be used in these kinds of areas. In order to ensure that buildings in these areas can be mapped, it is important that 2D and 3D features are analysed in order to get a high level of accuracy when classifying the area. The aim of this research, then, is to assess the effectiveness of object-oriented image analysis software eCognition (Definiens Imaging, Germany) in urban environments that include features such as buildings, roads, car parks and vegetation. This is done by combining high spatial resolution mosaic-Orthoimages and DSM layers in order to be able to group features of the environment. This method is superior to VHR imagery as UAV Orthoimages are able to combine object segmentation and the fuzzy dimension digital classification method to recognise features in a diverse environment, while objects may be too spectrally similar for VHR to be used effectively.

The site chosen for this research was the Jordan University of Science and Technology (JUST). Founded in 1986 and designed by the Japanese architect Tange, the campus combines futuristic style and sustainability. It is located 70 km north of the capital Amman and 6 km south of Al-Ramtha at 32˚28'36.77"N and longitude 35˚58'24.05"E as shown in Figure 1. The campus has an elevation of 580 m and covers an area of 11 km², which includes both buildings and natural areas. JUST is generally divided into two halves, the medical faculties, which can be seen on the lower part of Figure 1 and the engineering faculties which can be found on the upper part of Figure 1. The buildings tend to follow two main axis, the academic spine, where lecture buildings can be found, and the social spine, which includes services such as the library, mosque and accommodation.

MARSRobotics® Talon with fixed wings, as seen in Figure 2(a), was used as the UAV in this study and performed all of the flights. This UAV complies with design standards for UAVs and is approved by Transport Canada, the Jordan Civil Aviation Regulation Commission (JCARC), as well as the Federal Aviation Administration (FAA) in the USA. MARSRobotics® Talon is a hand-launched at takeoff. It has a 530 kV brushless motor which is powered by two 6-cell 4500 mAh batteries which provide it with two hours of flight time with a full payload. When cruising, it can reach speeds of 72 km/h (20 m/s) and is able to reach maximum speeds of 85 km/h (23.6 m/s). It is also able to operate in up to 35 km/h winds when flying and 25 km/h when the parachute has been deployed. It can be controlled remotely within a 15 km radius by a handheld controller or can make use of Pixhawk software created by PX4 and manufactured by 3D robotics which allows the MARSRobotics® Talon of autonomous flight. The maximum weight at takeoff is anything up to 3.5 kg (7.7 lbs) and the MARSRobotics® Talon can reach an altitude of 2000 m (3.1 miles) above sea level if needed. The controller displays data about the flight, such as altitude, battery status and distance travelled. The following table shows the technical features of the MARSRobotics® Talon (Table 1).

Figure 2. MARSRobotics® UAV (a) and Sony Alpha ILCE-A6000 camera (b).

The MARSRobotics® Talon features a SONY A6000 (ILCE-6000L) Digital Single-Lens Reflex (DSLR) camera, as seen in Figure 2(b), which is powered by its own rechargeable battery. It has a 24.3-megapixel Advanced Photo System (APS) Type-C (Classic) which involves a sensor, hybrid autofocus feature and a continuous shooting speed of up to 11 frames/second. It has a Complementary Metal-Oxide-Semiconductor (CMOS) sensor (23.5 × 15.6 mm). The data is recorded in the form of 8-bit, in both JPEG and RAW formats with a resolution of 4000 × 6000 pixels. The lens ranges from 16 - 50 mm and has power zoom with an 83˚ - 32˚ angle of view as seen in Table 2. This camera is held on to the UAV by a gimbal, as this ensures a constant viewing angle, meaning that near-nadir images will be provided.

The way in which the flight is controlled is crucial to the MARSRobotics® Talon. Drones such as this can be controlled in a variety of ways, such as GPS enabled autopilot systems or through using radio-controlled hardware. In this study, the Pixhawk autopilot system was used to control the UAV. This is an open-source autopilot system which is marketed towards users of inexpensive autonomous