Urban Remote Sensing. Группа авторов

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After a disaster, accurate and swift searching for the affected population and understanding their needs (e.g. water, food, medical assistance, etc.) is urgent and challenging. With the aid of UAS, humanitarian relief can be more efficiently delivered. Bravo et al. (2019) developed a Partially Observable Markov Decision Process (POMDP) to improve UAS path planning by giving higher priorities to the areas that are more likely to have victims. They showed that the method can provide the full coverage of the affected area and optimize the time to find the victims groups in three different cases, namely a tornado in Xanxerê, Brazil, a refugee camp in South Sudan, and a nuclear accident in Fukushima, Japan, which only took no more than 10 minutes, 5 minutes, and 2 hours, respectively (Bravo et al., 2019). However, they also mentioned that the use of UAS can be limited by the location, sight, limited base distance, battery life, and the concerns of social acceptance and airspace access.

      3.4.2 BUILDING INSPECTION

      The built environments we live in are constantly aging and wearing down. Regular checking of rooftops is critical to identify wet insulation issues at an early stage and prolong the lifespan of building roofs. Heat transfer and air loss through windows, cracks, chimneys are important causes of energy loss in residential buildings. Therefore, infrastructure inspection is necessary to maintain their energy efficiency and reduce further maintenance costs. UAS can capture thermal patterns with infrared cameras and generate 3D Computer‐aided Design (CAD) models, which can help to assess energy production and conservation of urban structures (Rakha and Gorodetsky, 2018). Some efforts have been made to advance UAS applications in building inspections. For example, Zhang et al. (2015) developed a relative thermographic calibration algorithm and automatic thermal anomaly detection methods. Besada et al. (2018) demonstrated an automated flight process achieved with a Mission Definition System for infrastructure inspections using UAS. However, there are still some issues concerning the UAS applications in building energy audits. For example, better GPS accuracy is needed. The accuracy of UAS and its ability to avoid obstacles relies heavily on the accuracy of the onboard GPS and Inertial Navigation System (INS) (Steffen and Förstner, 2008). There are no conclusive strategies for path planning or inspection distance optimization for the UAS‐based building audit, which points toward a new research direction (Rakha and Gorodetsky, 2018).

      3.4.3 PHYSICAL DISORDER DETECTION

      The physical characteristics of neighborhoods are important information for analyzing the effects of place on social problems. However, this information is usually unavailable or measured with aggregated secondary data that are only suitable for large‐scale investigations. Grubesic et al. (2018) used UAS to evaluate hyper‐local information on physical disorders like litter, unkempt lots, and building decay. They conducted two low‐altitude missions with an Ebee, a fix‐wing model from senseFly, at 65 m flying height, 60% lateral overlap, and 75% longitudinal overlap to survey two neighborhoods in Phoenix, Arizona. In each neighborhood, a total of 91.96 and 45.18 acres were covered with 382 and 217 RGB images, respectively. The resulting UAS imagery is much sharper than common medium‐resolution satellite imagery and makes it easier to discern small objects like air‐conditioning units. Compared to omnidirectional street imagery, UAS imagery is not influenced by temporal mismatches or blocked sight caused by depth perspective. The UAS approach also turned out to be more economical than the traditional way to collect hyper‐local data through systematic social observation (SSO) or neighborhood audits.

      3.4.4 SMART CITIES

Smart city is an idea to integrate technologies to support a healthy, safe, and convenient way of urban living. In the realization of smart cities, UAS can play a role in monitoring, detecting, and predicting anomalies in urban environments and therefore safeguarding humans. Because implementing real UAS experiments in urban environments is risky concerning safety and privacy, simulation may be needed to test and evaluate the performance of UAS in advance. For example, Pannozzi et al. (2019) created a simulated 3D urban environment with the aid of OpenStreetMap (OSM) volunteered geographic information for the city of Turin, Italy. The virtual city model considers wind, battery, and motor failure. Under this environment, they have successfully achieved autonomous and semi‐autonomous UAS missions using the PX4 package, and a full manual piloting flight using the Parrot‐Sphinx package. Based on their test results, they concluded UAS can be used in traffic and crowd monitoring, 3D building mapping, and urban growth to better understand urban dynamics.

3.5 CASE STUDY: MAPPING AN URBAN RECREATION COMPLEX WITH UAS

To better understand the general workflow for UAS data processing, a case study is presented here. This project involved the use of a small UAS to generate 2D and 3D mapping data over a relatively small urban recreation complex through the photogrammetry software package called Agisoft Metashape. For this project, researchers used a small UAS (DJI Phantom 4 Pro) to obtain oblique imagery of an urban recreation complex owned by the City of Auburn, Alabama, USA, and created 2D and 3D mapping data. The workflow used for this project largely followed the same procedure outlined by Nex and Remondino (2014). Since the researchers were interested in generating 3D outputs from the UAS imagery, a flight plan was designed as a double‐grid pattern that would allow the UAS to maintain an oblique camera orientation during the image collection process, which allowed the facades of the buildings and other tall features to be better captured in the imagery. Figure 3.2a depicts the AOI for the mapping mission and Figure 3.2b illustrates the double grid flight plan overlaid on the AoI. This project used the application Pix4D Capture (pix4d.com/product/pix4dcapture) to design and operationalize the UAS image collection flight plan. There are numerous flight planning applications available that have the same core functionality of Pix4D Capture but offer minor differences in user customization and interface design. Some examples of other similar flight planning applications include DroneDeploy [Flight App for Business] (www.dronedeploy.com/product/mobile), Map Pilot by Maps Made Easy (www.mapsmadeeasy.com), DJI Ground Station Pro (www.dji.com/ground‐station‐pro), and Litchi (www.flylitchi.com).

FIGURE 3.2 (a) An aerial view of the Urban Recreation Complex. (b) A flight plan design for UAS data collection. Since the UAS uses oblique camera orientation when capturing images, it needs to flight two grid patterns to capture all four sides of every feature.

      To create the desired 3D data outputs (namely, point cloud and a textured model) for this project the user‐defined flight parameters for the flight plan included a flight altitude of 35 m above ground level (AGL), a frontal image overlap of 85%, and side image overlap of 80%, and an oblique camera orientation of 65° (i.e. 90° is nadir orientation). The flight altitude of 35 m allowed the UAS to fly the double‐grid pattern at a low altitude, leading to high‐resolution images to be collected. As previously mentioned, these flight parameters are highly contingent on the specific context that a UAS operator is conducting flights for so their settings can vary from one project to another. For this urban recreation complex mapping project, there were no obstacles of concern above approximately 30 m AGL, so 35 m was a safe flying altitude that would also allow imagery to be acquired at very high resolution. The benefits of this low flight altitude can be seen in the increased quality of resolution in the aerial images, thus leading to a much finer resolution СКАЧАТЬ