Low-altitude Unmanned Aerial Vehicle Research Articles

Satellite monitoring problems in the aerospace complex

Задачі підсуNowadays with the rapid development of information technologies, UAV-based Remote Sensing (drone remote sensing) gives a new opportunities for conducting scientific research in a much more detail way. UAVs (unmanned aerial vehicles) give the opportunity to acquire data at sufficiently low cost. They also provide remote data more rapidly than piloted aerial vehicles. Nowadays drones are often used, because application of piloted aerial vehicles can be dangerous, difficult and expensive for some territories. Application of low altitude UAVs give a possibility to achieve images with a very high resolution and sufficient precision. In this article structure and main details of drones were considered. It also was noted, that technologies of UAV-based Remote Sensing are used in different areas.Agricultural drones help to analyze crops, make decisions on how to use the crop information and take the necessary actions to correct the problems. These unmanned aerial vehicles let to see fields from the sky. Agricultural drones are used to help increase crop production and monitor crop growth. Drones and sensors give a detail picture of fields. They can survey the fields periodically. Agricultural drones can reveal many issues such as soil variation, pest infestations and changes in the crops over time. They also show differences between healthy and unhealthy plants. Drones are flied over the crops and help to make decisions on how to proceed given the crop information. Nowadays there is a large capacity for growth in the area of agricultural unmanned aerial vehicles. With technology constantly improving, imaging of the crops will need to improve as well.Drones are used for exploring for minerals and mapping deposit sites, they are used in the oil and gas industry for remote monitoring. Drones can provide information of nature disasters and give help to assess property damage. They help to conduct forest monitoring and to assess plant health. Unmanned aerial vehicles are also used in a military capacity and ecological monitoring. It also was noted, that there is a large capacity for development and improvement of unmanned aerial vehicles.путникового моніторингу в аерокосмічному комплексі

Radiological Identification of Near-Surface Mineralogical Deposits Using Low-Altitude Unmanned Aerial Vehicle

An ever-increasing global population and unabating technological growth have resulted in a relentless appetite for mineral resources, namely rare earth elements, fuel minerals and those utilised in electronics applications, with the price of such species continuing to climb. In contrast to more established large-scale and high-cost exploration methodologies, this work details the application of novel multi-rotor unmanned aerial vehicles equipped with miniaturised radiation detectors for the objective of undertaking resource exploration at lower costs, with greater autonomy and at considerably enhanced higher spatial resolutions; utilizing the ore material’s inherent low levels of characteristic radioactivity. As we demonstrate at the former Wooley Mine site in Arizona, USA, a legacy Cu/Fe prospect where the 600 by 275 m ore body (with a maximum deposit depth of 150 m), it is shown that such a fusion of commercially available low-altitude multi-rotor aerial technology combined with cutting-edge micro-electronics and detector materials is capable of accurately assessing the spatial distribution and associated radiogenic signatures of commercially valuable surface/near-surface ore bodies. This integrated system, deployed at an autonomously controlled consistent survey altitude and using constant grid transects/separations, is shown to be able to delineate the mineral-containing ore deposits on the site, the location(s) of former mine workings and other surface manifestations. Owing to its advantageous costs alongside its ease of operation and subsequent data-processing, through the adoption of this system, it is envisaged that less economically developed countries would now possess the means through which to evaluate and appropriately quantify their mineral wealth without the significant initial expenditure needed to equip themselves with otherwise prohibitively expensive technologies.

Space-air-ground integrated vehicular network for connected and automated vehicles: Challenges and solutions

Unlimited and seamless coverage as well as ultra-reliable and low-latency communications are vital for connected vehicles, in particular for new use cases like autonomous driving and vehicle platooning. In this paper, we propose a novel Space-Air-Ground integrated vehicular network (SAGiven) architecture to gracefully integrate the multi-dimensional and multi-scale context-information and network resources from satellites, High-Altitude Platform stations (HAPs), low-altitude Unmanned Aerial Vehicles (UAVs), and terrestrial cellular communication systems. One of the key features of the SAGiven is the reconfigurability of heterogeneous network functions as well as network resources. We first give a comprehensive review of the key challenges of this new architecture and then provide some up-to-date solutions on those challenges. Specifically, the solutions will cover the following topics: (1) space-air-ground integrated network reconfiguration under dynamic space resources constraints; (2) multi-dimensional sensing and efficient integration of multi-dimensional context information; (3) real-time, reliable, and secure communications among vehicles and between vehicles and the SAGiven platform; and (4) a holistic integration and demonstration of the SAGiven. Finally, it is concluded that the SAGiven can play a key role in future autonomous driving and Internet-of-Vehicles applications.

Interference Modeling in Low-Altitude Unmanned Aerial Vehicles

With the emergence of new IoT wireless access technologies, shared frequency bands are expected to see unprecedented congestion levels. Using low-altitude unmanned aerial vehicles (UAVs) for provisioning IoT coverage would require a very careful consideration for the existing interference levels in the radio spectrum. This letter presents a tractable analytic framework developed using tools from stochastic geometry to characterize the spectrum occupancy as seen from a UAV perspective. The framework is supported by empirical measurements conducted using a software defined receiver mounted on a UAV.

Comparison of Object Detection and Patch-Based Classification Deep Learning Models on Mid- to Late-Season Weed Detection in UAV Imagery

Mid- to late-season weeds that escape from the routine early-season weed management threaten agricultural production by creating a large number of seeds for several future growing seasons. Rapid and accurate detection of weed patches in field is the first step of site-specific weed management. In this study, object detection-based convolutional neural network models were trained and evaluated over low-altitude unmanned aerial vehicle (UAV) imagery for mid- to late-season weed detection in soybean fields. The performance of two object detection models, Faster RCNN and the Single Shot Detector (SSD), were evaluated and compared in terms of weed detection performance using mean Intersection over Union (IoU) and inference speed. It was found that the Faster RCNN model with 200 box proposals had similar good weed detection performance to the SSD model in terms of precision, recall, f1 score, and IoU, as well as a similar inference time. The precision, recall, f1 score and IoU were 0.65, 0.68, 0.66 and 0.85 for Faster RCNN with 200 proposals, and 0.66, 0.68, 0.67 and 0.84 for SSD, respectively. However, the optimal confidence threshold of the SSD model was found to be much lower than that of the Faster RCNN model, which indicated that SSD might have lower generalization performance than Faster RCNN for mid- to late-season weed detection in soybean fields using UAV imagery. The performance of the object detection model was also compared with patch-based CNN model. The Faster RCNN model yielded a better weed detection performance than the patch-based CNN with and without overlap. The inference time of Faster RCNN was similar to patch-based CNN without overlap, but significantly less than patch-based CNN with overlap. Hence, Faster RCNN was found to be the best model in terms of weed detection performance and inference time among the different models compared in this study. This work is important in understanding the potential and identifying the algorithms for an on-farm, near real-time weed detection and management.

Controller for a Low-Altitude Fixed-Wing UAV on an Embedded System to Assess Specific Environmental Conditions

The use of an appropriate sensor on an unmanned aerial vehicle (UAV) is vital to assess specific environmental conditions successfully. In addition, technicians and scientists also appreciate a platform to carry the sensors with some advantages such as the low costs or easy pilot management. However, extra requirements like a low-altitude flight are necessary for special applications such as plant density or rice yield. A rotary UAV matches this requirement, but the flight endurance is too short for large areas. Therefore, in this article, a fixed-wing UAV is used, which is more appropriate because of its longer flight endurance. It is necessary to develop an own controller system to use special sensors such as Lidar or Radar on the platform as a multifunctionality system. Thereby, these sensors are used to generate a digital elevation model and also as a collision avoidance sensor at the same time. To achieve this goal, a small UAV was equipped with a hardware platform including a microcontroller and sensors. After testing the system and simulation, the controller was converted into program code to implement it on the microcontroller. After that, several real flights were performed to validate the controller and sensors. We demonstrated that the system is able to work and match the high requirements for future research.

Secure Routing Algorithm in Wireless Mesh Network

In the today’s’ era of communication technology disaster management plays important role in life saving. Regardless of men made disasters or natural disasters proper communication network can be used to exchange information. In natural calamities wireless sensor mesh network proved to be as good option for communication. Low-altitude Unmanned Aerial Vehicles (UAVs) which is associated with WLAN Mesh Networks (WMNs) can be used in disaster management areas. The advantage of it is it can be installed on demand and it can use for efficient exploration of sized areas. The WMN is consisting of different components such as mesh clients, mesh routers and base station. It is more prone to have attacks of different types such as Denial of Service attack, black hole, gray hole , reply attack, Sybil attack etc. The most important attack which can damage whole working of WMN is routing attack. IEEE 802.11i is used as standard protocol for security in WLAN and IEEE 802.11s is used as secure algorithm for communication in WLAN mesh network. But both of them fail to address issue of routing problem in WLAN mesh network. The primary purpose of this paper is to outline routing algorithm and to design assured routing protocol. In this paper we have proposed improved secure Position-Aware, Secure, and Efficient mesh Routing approach (S-PASER). The proposed mechanisms have obviated more attack than regular IEEE 802.11s/i security mechanism and well known, secure routing protocol. The proposed methodology can be implemented on omnet++ simulator. The simulation results proved to be have better performance and throughput than existing algorithm.

Air‐to‐ground channel model for UAVs in dense urban environments

A new analytical model of air-to-ground (A2G) channel between a low altitude unmanned aerial vehicle (UAV) and a mobile station is derived. It is proposed for the dense urban environment at frequencies of (1, 2 and 5.8) GHz. The model terms are determined on the basis of ray-tracing simulation analysis for A2G channel and knife-edge diffraction theory. The model is compared with the other A2G model and shows better results of standard error and confidence interval validation. Furthermore, the effects of changing the frequency and UAV height on path loss and probability of line of sight are discussed. Therefore, the proposed model provides a simple and good estimation compared to the complicity of ray-tracing simulation.

Development of Hierarchical UAS Traffic Management (UTM) in Taiwan

The unmanned aerial vehicles (UAVs) will soon be legally flying into airspace in Taiwan and many countries. UAS traffic management (UTM) becomes an important issue to challenge aviation safety. A hierarchical UTM is proposed and constructed based on the concept of Air Traffic Management (ATM) for the manned aviation system. The proposed Regional UTM (RUTM) and National UTM (NUTM) are hierarchical infrastructure to offers traffic management services for all legal UAV flights in Taipei Flight Information Region (TPE FIR) under and over 400 feet. The proposed UTM provides UAV/pilot log-in/out, flight planning, geo-fencing, detect and avoid (DAA), logbook records, big data analysis, ground control redundant support and controller-pilot communication on multi-window monitor system. UTM data are encrypted to prevent hijacking threat to low altitude UAVs. The UTM adopts ADS-B Like communication infrastructure to offer affordable cost for all UAVs with high reliable surveillance data reporting. The ADS-B Like surveillance mechanism includes 4G/LTE, LoRa, APRS and XBee. Their gateway deployment is designed and constructed with supports of Chung Hua Telecommunication (CHT) in urban and suburban areas. While those remote areas, such as mountain and deep forest, the National Forest Bureau workstation will be selected. The ADS-B Like communication will cover full airspace in Taiwan. Surveillance data reliability, continuity, integrity and availability will be examined by flight tests. The proposed Hierarchical UTM will launch trial runs in Tainan County of RUTM operation to verify the proposed concept, system performance and general operation into the national “UAS Innovative Experiment Project”.

Propagation Measurements and Modeling for Low Altitude UAVs From 1 to 24 GHz

In most countries, small (<2 kg) and medium (<25 kg) size unmanned aerial vehicles (UAVs) must fly at low altitude, below 120 m, and with permanent radio communications with ground for control and telemetry. These communications links can be provided using 4G/5G networks or dedicated links, but in either this case the communications can be significantly degraded by frequent Non Line of Sight (NLoS) propagation. In this case, reflection and diffraction from ground objects are critical to maintain links, and hence accurate propagation models for this must be considered. In this letter we present a model for path loss when the UAV is flying in NLOS conditions. The study is based on measurements made at frequencies of 1, 4, 12, and 24 GHz with a UAV flying in a suburban environment. Measurements have been used to model NLOS propagation below 4 GHz, where the dominant mechanism is diffraction, and above 4 GHz where multipath is the dominant propagation mechanism. The model can be of use in predicting excess losses when UAVs fly in suburban NLOS conditions.

Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging

Rapid and accurate biomass and yield estimation facilitates efficient plant phenotyping and site-specific crop management. A low altitude unmanned aerial vehicle (UAV) was used to acquire RGB and hyperspectral imaging data for a potato crop canopy at two growth stages to estimate the above-ground biomass and predict crop yield. Field experiments included six cultivars and multiple treatments of nitrogen, potassium, and mixed compound fertilisers. Crop height was estimated using the difference between digital surface model and digital elevation models derived from RGB imagery. Combining with two narrow-band vegetation indices selected by the RReliefF feature selection algorithm. Random Forest regression models demonstrated high prediction accuracy for both fresh and dry above-ground biomass, with a coefficient of determination (r2) > 0.90. Crop yield was predicted using four narrow-band vegetation indices and crop height (r2 = 0.63) with imagery data obtained 90 days after planting. A Partial Least Squares regression model based on the full wavelength spectra demonstrated improved yield prediction (r2 = 0.81). This study demonstrated the merits of UAV-based RGB and hyperspectral imaging for estimating the above-ground biomass and yield of potato crops, which can be used to assist in site-specific crop management.

Landslides Information Extraction Using Object-Oriented Image Analysis Paradigm Based on Deep Learning and Transfer Learning

How to acquire landslide disaster information quickly and accurately has become the focus and difficulty of disaster prevention and relief by remote sensing. Landslide disasters are generally featured by sudden occurrence, proposing high demand for emergency data acquisition. The low-altitude Unmanned Aerial Vehicle (UAV) remote sensing technology is widely applied to acquire landslide disaster data, due to its convenience, high efficiency, and ability to fly at low altitude under cloud. However, the spectrum information of UAV images is generally deficient and manual interpretation is difficult for meeting the need of quick acquisition of emergency data. Based on this, UAV images of high-occurrence areas of landslide disaster in Wenchuan County and Baoxing County in Sichuan Province, China were selected for research in the paper. Firstly, the acquired UAV images were pre-processed to generate orthoimages. Subsequently, multi-resolution segmentation was carried out to obtain image objects, and the barycenter of each object was calculated to generate a landslide sample database (including positive and negative samples) for deep learning. Next, four landslide feature models of deep learning and transfer learning, namely Histograms of Oriented Gradients (HOG), Bag of Visual Word (BOVW), Convolutional Neural Network (CNN), and Transfer Learning (TL) were compared, and it was found that the TL model possesses the best feature extraction effect, so a landslide extraction method based on the TL model and object-oriented image analysis (TLOEL) was proposed; finally, the TLOEL method was compared with the object-oriented nearest neighbor classification (NNC) method. The research results show that the accuracy of the TLOEL method is higher than the NNC method, which can not only achieve the edge extraction of large landslides, but also detect and extract middle and small landslides accurately that are scatteredly distributed.

A survey of radio propagation channel modelling for low altitude flying base stations

The increased utilization of unmanned aerial vehicles (UAVs) in the commercial market and military on account of their agility, nonpiloted and easy manoeuvering leads their applications in the telecommunication sector as well. It is expected that UAVs will play a vital role in 5G and Beyond 5G (B5G) networks as flying base stations (BSs) and/or relays. Recently, they are also proposed to assist the existing terrestrial communication infrastructure in forthcoming 5G/B5G to provide improved wireless network coverage particularly to the areas difficult to reach, the scenarios demanding high data rate and low latency on emergency needs, transceiving sensors data from field to the ground servers and providing wireless network coverage in a disaster where existing terrestrial communication infrastructure gets partially/severely damaged. However, it is of an utmost challenge to model the radio propagation channel from a UAV (low altitude platforms) to existing terrestrial BSs, the receiver on ground and with other flying UAVs in a network. This paper provides a survey of both measurement and simulation based radio propagation channel modelling investigations for a low altitude UAV enabled wireless network. Furthermore, the potential open research gaps and use cases are highlighted which will be key to define the role of UAVs in future wireless networks for various applications.

Evaluation of a UAV-mounted consumer grade camera with different spectral modifications and two handheld spectral sensors for rapeseed growth monitoring: performance and influencing factors

The objective of this study was to evaluate the crop monitoring performance of a consumer-grade camera with non-modified and modified spectral ranges which are commonly used in low-altitude unmanned aerial vehicle (UAV) platforms. The camera was fixed sequentially with seven types of filters for collecting visible images and near-infrared (NIR) images with different center band locations and bandwidths. Meanwhile, field-based hyperspectral data and normalized difference vegetation index (NDVI) measured by a GreenSeeker handheld crop sensor (GS-NDVI) were collected to examine the accuracy of rapeseed growth monitoring in terms of vegetation indices (VIs) derived from UAV images. Results showed that the UAV-based RGB-VIs and optimal NIR-VIs had similar accuracy for predicting GS-NDVI. Moreover, similar results were achieved based on the hyperspectral data, indicating the importance of spectral characteristics for GS-NDVI estimation. However, the UAV-based results also indicated that the performance of VIs derived from the band combinations containing longer NIR center wavelengths and narrower bandwidths was obviously poorer than that of the RGB-VIs. The image quality of the NIR band was also found to be inferior to the visible band based on quantitative analysis, which also revealed that image quality had great impact on UAV-based results. Image quality was then related to the effects of camera exposure, spectral sensitivity, soil background and dark areas. The results from this study provide useful information for camera modifications by selecting appropriate filters that not only are sensitive to crop growth, but also ensure image quality.

Spatial Coordinates Correction Based on Multi-Sensor Low-Altitude Remote Sensing Image Registration for Monitoring Forest Dynamics

Tree species diversity plays a significant role in our ecosystem. In order to monitor forest dynamics, hyperspectral remote sensing equipped on a small unmanned aerial vehicle (UAV) is commonly applied, such as individual tree detection and classification. However, low resolution, positioning errors and the imaging perspective of small UAV affected by wind speed/direction, complex terrain, battery capacity, aircraft posture, flying height and other human factors result in relatively large positional errors (i.e., GPS errors) in such hyperspectral images, and the precise forest dynamics monitoring is limited, especially in spatial analysis. In order to reduce the positional errors of hyperspectral images captured from a small UAV and provide a precise forest dynamics monitoring, we present a novel spatial coordinates correction approach by registering low-altitude UAV visible light and hyperspectral images. The proposed method first employs visible light images and ground control points to stitch a geographic coordinate system as our groundtruth. Hyperspectral images (UHI) are then registered onto the stitched visible light image (UVI) via a novel image registration method. Finally, spatial coordinates of the registered hyperspectral images are updated by using the aforementioned groundtruth. Extensive experiments on image registration and spatial coordinates correction demonstrate the favorable performance of our method. Compared against four state-of-the-art registration methods, our method shows the best registration performance, and the positional errors of hyperspectral images are significantly reduced. Such accuracy is considered very high in this research.

Hierarchical Collaboration Approach for Tracking of Flying Target by UAVs

This paper investigates a hierarchical target tracking method based on the collaboration of unmanned aerial vehicles (UAVs) at different altitudes. To enhance the target tracking range, the high-altitude UAVs monitor the wide area, and transmit their surveillance information to low-altitude UAVs which directly detect and collect information about the target's movements. The contributions of this paper are threefold: First, to track the flying target in a dynamic environment, a modified Lyapunov guidance vector field (LGVF) method is used to plan velocities for UAVs, where a time-varying vertical component is incorporated into the traditional LGVF function to satisfy the constraints of cluster communication. Secondly, a three-dimensional local collision-free guidance vector field (TLCGVF) method is proposed for UAVs to plan collision-free paths on line. To simultaneously track the target and avoid obstacles, the vector field by LGVF is used as the original vector field of TLCGVF. Thirdly, the rolling optimization strategy is used to adjust the reactive parameters of TLCGVF to enhance the path quality. The simulation results confirm the feasibility of the above approach.

Low Altitude Unmanned Aerial Vehicles (UAVs) and Satellite Remote Sensing Are Used to Calculated River Discharge Attenuation Coefficients of Ungauged Catchments in Arid Desert

The arid desert ecosystem is very fragile, and the change of its river discharge has a direct impact on irrigation and natural environment. River discharge attenuation coefficients is a key index to reveal the stability of desert river ecosystem. However, due to the harsh conditions in desert areas, it is difficult to establish a hydrological station to obtain data and calculate the attenuation coefficients, so it is urgent to develop new methods to master the attenuation coefficients of rivers. In this study, Taklamakan desert river was selected as the research area, and the river discharge of the desert river were estimated by combining low-altitude UAV and satellite remote sensing technology, so as to calculate the attenuation status of the river in its natural state. Combined with satellite remote sensing, the surface runoff in the desert reaches of the Hotan River from 1993 to 2017 were estimated. The results showed that the base of runoff attenuation in the lower reaches of the Hotan River is 40%. Coupled UAV and satellite remote sensing technology can provide technical support for the study of surface runoff in desert rivers within ungauged basins. Using UAV and satellite remote sensing can monitor surface runoff effectively providing important reference for river discharge monitoring in ungauged catchments.

Energy-Efficient Data Collection and Device Positioning in UAV-Assisted IoT

The Internet of Things (IoT) will significantly change both industrial manufacturing and our daily lives. Data collection and 3-D positioning of IoT devices are two indispensable services of such networks. However, in conventional networks, only terrestrial base stations (BSs) are used to provide these two services. On the one hand, this leads to high energy consumption for devices transmitting at cell edges. On the other hand, terrestrial BSs are relatively close in height, resulting in poor performance of device positioning in elevation. Due to their high maneuverability and flexible deployment, unmanned aerial vehicles (UAVs) could be a promising technology to overcome the above shortcomings. In this article, we propose a novel UAV-assisted IoT network, in which a low-altitude UAV platform is employed as both a mobile data collector and an aerial anchor node to assist terrestrial BSs in data collection and device positioning. We aim to minimize the maximum energy consumption of all devices by jointly optimizing the UAV trajectory and devices’ transmission schedule over time, while ensuring the reliability of data collection and required 3-D positioning performance. This formulation is a mixed-integer nonconvex optimization problem, and an efficient differential evolution (DE)-based method is proposed for solving it. Numerical results demonstrate that the proposed network and the optimization method achieve significant performance gains in both energy-efficient data collection and 3-D device positioning, as compared with a conventional terrestrial IoT network.

Multi-Sensor UAV Tracking of Individual Seedlings and Seedling Communities at Millimetre Accuracy

The increasing spatial and temporal scales of ecological recovery projects demand more rapid and accurate methods of predicting restoration trajectory. Unmanned aerial vehicles (UAVs) offer greatly improved rapidity and efficiency compared to traditional biodiversity monitoring surveys and are increasingly employed in the monitoring of ecological restoration. However, the applicability of UAV-based remote sensing in the identification of small features of interest from captured imagery (e.g., small individual plants, &lt;100 cm2) remains untested and the potential of UAVs to track the performance of individual plants or the development of seedlings remains unexplored. This study utilised low-altitude UAV imagery from multi-sensor flights (Red-Green-Blue and multispectral sensors) and an automated object-based image analysis software to detect target seedlings from among a matrix of non-target grasses in order to track the performance of individual target seedlings and the seedling community over a 14-week period. Object-based Image Analysis (OBIA) classification effectively and accurately discriminated among target and non-target seedling objects and these groups exhibited distinct spectral signatures (six different visible-spectrum and multispectral indices) that responded differently over a 24-day drying period. OBIA classification from captured imagery also allowed for the accurate tracking of individual target seedling objects through time, clearly illustrating the capacity of UAV-based monitoring to undertake plant performance monitoring of individual plants at very fine spatial scales.

RAPID TOPOGRAPHIC MEASUREMENT AND THREE-DIMENSIONAL NUMERICAL MODELING METHOD FOR HIGH-STEEP/UPRIGHT SLOPES BASED ON AERIAL PHOTOGRAPHY OF UAV

The topographic measurement of high-steep to upright slopes of complex terrain and the establishment of three-dimensional numerical models have always been puzzling geologists. In recent years, the unmanned aerial vehicle(UAV)has been widely used in geological surveys due to its small size, maneuverability, and the ability to acquire high-resolution images. This paper focuses on a new measuring and modeling method for high-steep/upright slopes with integration of UAV and many software. Firstly, on the basis of low-altitude UAV tilt photography, the Agisoft Photoscan 3D real scene modeling software and reverse engineering-based Geomagic Studio's powerful point cloud data processing function, we generate the topographic maps of high-steep/upright slope with complex terrain quickly, combine with the topographic mapping function of CASS. Subsequently, the high-steep/upright slope closed CAD surface models of complex terrain are reconstructed utilizing the CAD surface modeling function of Geomagic Studio. Then we take the advantages of the powerful geometric processing and meshing ability of Hypermesh to mesh the CAD surface models, before establishing a fine three-dimensional(3D)numerical model for high-steep/upright slopes of complex terrain. Finally, the 3D numerical model is converted into a file recognizable by FLAC3D for calculation and analysis. In order to illustrate this method in details, we take the Feitian Waterfall Scenic Spot in Shenxianju Scenic Spot in Zhejiang Province as an example. The results indicate that the use of UAV enables fast, efficient and accurate mapping and 3D modeling of high-steep/upright slopes in complex terrain. The method has the advantages of being simple and practical, fast and convenient, and strong practicality.