Better regulations and wider acceptance from the general public are key to the growth of commercial drone use across the region.
Drones are playing an increasingly important role in optimising processes in various industries – providing efficiency and effectiveness while prioritising safety and savings. Their near limitless aerial perspective offers the ability to gather and analyse data, and when combined with artificial intelligence (AI), are revolutionising the way companies inspect, survey and map terrain, infrastructure and agriculture.
Malaysia based Aerodyne has carved a global name for itself by combining drones with AI’s powerful analytics. Apart from asset inspection, management and project monitoring in various sectors, Aerodyne also actively provides services in geospatial intelligence, emergency response, 2D and 3D mapping and precision agriculture.
A relative newcomer to the scene, the four-year-old company now has a presence in 24 countries and is ranked the world’s seventh best drone operator by Drone Industry Insights, a market research and analytics company based in Germany. Kamarul Muhamed, Aerodyne CEO and founder said that the growth in commercial applications for drones has resulted in global interest for his company’s unique AI-driven services. However, he admitted that most of his subsidiaries are based outside of Southeast Asia.
Playing catch-up
Apart from its business in Malaysia, Aerodyne is also active in Indonesia and has contracts in Singapore and Brunei as well. Though he praised the Philippines for being early adopters of the technology, Kamarul said that drones and their accompanying services are tightly regulated in most ASEAN countries.
“In general, ASEAN has adopted a wait-and-see approach to drone use – but we should be taking a leadership role,” Kamarul told The ASEAN Post this week.
“We have inherited a lot of regulations which do not necessarily promote or support (drone) innovation. There are some regulations now based on drones’ usage, their weight and the height they are flown at, but there is no licensing requirement. If we have licensing, we can regulate the industry better,” he stressed.
While Kamarul pointed out that rules are especially tight in countries like Lao PDR and Myanmar, there is no region-wide consensus on drone regulations. Even in Singapore, which has long been at the forefront of technology in ASEAN, lawmakers are still reviewing changes to drone legislature that was proposed by the Civil Aviation Authority of Singapore (CAAS) last April. Among the changes include a pilot licensing scheme, a compulsory online training programme and stricter requirements such as partial or full certification for heavier unmanned aircraft.
Kamarul used BVLOS (Beyond Visual Line of Sight) as an example of legislation playing catch-up with technology. BVLOS is a concept which allows drone operators to gather data over large areas without having to be in close proximity to the drone and is seen as the next step forward for the industry.
“Traditionally, it has been seen as dangerous and irresponsible – but the technology is increasingly maturing. If you certify the operation, it is very safe,” he said.
Market potential
Explaining that while drone adoption is about to go mainstream globally, Kamarul said ASEAN only represents less than three percent of the global drone market which is forecasted to be worth US$127.3 billion in 2020. However, he added that it still represents a substantial sum and there is huge potential within the rapidly growing region. There are a lot of services that can be unlocked in terms of efficiency, and this is especially true in countries which are just building their infrastructure and are looking at cost-efficient ways to maintain them.
Apart from a framework which supports businesses and innovation, there is a need to educate the public on the benefits of drones and their role in building cities of the future. Safety and privacy are the public’s two biggest concerns surrounding drone use, but their increasing adoption in our daily lives is helping to change negative stereotypes.
Airbus successfully trialled the world’s first shore-to-ship delivery in Singapore in March, and in January, Chinese e-commerce platform JD.com conducted what is believed to be Southeast Asia’s first government-approved drone delivery by delivering backpacks and books to Indonesian students in a rural school more than 250 kilometres away. In another regional first, Thailand started using drones to address worker shortages in the farming sector last year, deploying them to help map and survey crops as well as spray fertilisers and pesticides.
While these trials are a good first step, ASEAN member countries should consider implementing training programmes and licencing to better regulate the industry. With drones set to play an increasingly prominent part in the region’s economy, key policies have to be put in place now to ensure better integration without negative effects on society.
Jason Thomas
12 April 2019
How can today's advanced technology solve the challenges that many organizations face after obtaining vast 3D point cloud datasets, including the management, storage, registration, fusion and extraction of useful and actionable information?
Instruments for digitizing the 3D real environment are becoming smaller, more lightweight, lower-cost and more robust. Accordingly, they are finding increasingly widespread usage, not only on surveying tripods for the highest accuracy, but also on mobile platforms such as autonomous vehicles, drones, helicopters, aircraft, robotic vacuum cleaners, trains, mobile phones, satellites and Martian rovers. Lidar uses laser scanning, while photogrammetry records images from one or more cameras which may be moving. Each laser scan records tens of millions of data point positions and colours in a point cloud, and hundreds of such point clouds may be combined. This article discusses the challenges that many companies and organizations face after obtaining vast 3D point cloud datasets, including the management, storage, registration, fusion and extraction of useful and actionable information.
Cloud computing
The first challenges users face in performing 3D point cloud data processing include:
Data Storage: The amount of data recorded grows exponentially with time, creating large data repositories.
Processing: The computing power required increases as new algorithms with useful functionality are released and with the volume of data.
Sharing: There are multiple stakeholders spread geographically around the world on mobile platforms who all need to view the most up-to-date data at the same time.
Previously, a software application ran on a dedicated server in a data centre but, if the computer hardware broke down, the user either had to find a backup (which had to be standing by and ready) or would suffer an interruption in service. Many companies guarantee a 24/7 level of service and so cannot tolerate this. However, Cloud Computing now gives users access, over a network, to applications running on a set of shared or pooled servers in a globally communicating network of data centres, giving speed and productivity improvements, resulting in increased competitiveness.
Figure 1: 30 Terrestrial laser scans of a central London library, fully automatically aligned using the Vercator software.
Big data analytics
Users face the difficult challenge of how to boil down the vast amounts of 3D point cloud data to generate useful and actionable information. Current methods for creating Digital Twin BIM models of buildings require users to inspect vast 3D point clouds to manually recognize and mark the outline positions of surfaces, straight edges, walls, floors, ceilings, pipes, and objects, which is time-consuming and susceptible to error. Some semi-automatic methods on laptops require users to recognize and mark part of these and the program finds the rest. Again, such objects can be mislabelled. Fully automatic methods are becoming available on laptops but do not find all the useful information, so users must add and correct what is found. Sometimes the automatic method makes so many mistakes it is quicker for the user to find and mark the structures manually.
“Useful information” in one application may be different from that in another application. For example, in autonomous vehicles, it is an accurate 3D terrain model which can be used for safe navigation. In electricity pylon scanning, it is whether the pylon has its safety warning sign in place clearly visible and whether nearby vegetation is gradually encroaching on the power lines. In railway scanning, it is whether there has been any slippage or sag as well as an estimate of when gradually encroaching vegetation will become a hazard. Electricity supply companies and Network Rail are under UK government obligations to regularly inspect their assets and to perform preventative maintenance to ensure continuity of supply and travel.
Geometrical object recognition
Correvate has developed a suite of machine learning geometric image processing methods for fully automated basic object recognition – walls, floors (figures 2 and 3), edges (figure 4) and pipes (see figure 5).
See below for the rest of the article.
Figure 2: Automatic Wall and Floor Recognition in a recently poured concrete shell of a building under construction in London (16 aligned scans).
Figure 3: Automatic Wall Recognition in a recently poured concrete shell of a building under construction in London (16 aligned scans).
Figure 4: Automatic Edge Detection followed by fitting of straight-line segments in UCL circular/octagonal library under the iconic central dome (21 aligned scans)
Figure 5a (top), Pipe scan and 5b (bottom), Automatic Pipe Recognition in a Boiler Room 3.5 million point cloud; 98% cylinders correctly found (2 aligned scans red and blue).
Artificial Intelligence
Artificial neural networks are extremely simplified models of living brains, which are trained and learn like people rather than being programmed by a master programmer. The learned knowledge or skills are stored in a distributed manner in the strengths or weights of the neuron interconnections. Some artificial neural networks learn on their own while others require a teacher or instructor to tell them when they are right or wrong. Gradually, they get better and better at performing a task during the iterative learning cycles which usually take a long time and require thousands of examples of the training data. Artificial neural networks are particularly good at recognition, classification and optimization tasks. However, their performance depends crucially on how they are trained, the types and the amount of training data. Many types of neural network have been developed and, most recently, Convolutional Neural Networks (CNN) used to perform Deep Learning have become very popular and achieve very good results. In the case of object recognition, if the neural networks are only trained with examples of objects one wants to find, then all input data will be classified as one of those objects, even if it is not one of those objects. So, the performance of the neural network is only as good as the way it was trained and the data that was used to train it. Neural networks are not as new as you might imagine given their current popularity in the media. Over 30 years ago, Selviah (1989) proved that the weighted interconnection layer of neural networks performs the same operation as a collection of correlators, operating in parallel, matching images from a database with input data and then the non-linear part of the neurons decide which image matches the input most closely. The clever part is the way in which the training automatically works out what images to store in the database in the first place.
In the conference room image, figure 6, you see the impressive recognition results after training a new type of CNN with data from the Stanford Large-Scale 3D Indoor Spaces Dataset (S3DIS) using 70,000 3D objects of 13 types, structural objects: ceiling, floor, wall, beam, column, window, door, and movable objects: table, chair, sofa, bookcase, board and clutter in 11 types of room. Each category of object is marked in a different colour, for example, ‘chairs’ are marked in yellow, ‘boards’ are marked in orange, ‘beams’ are marked in red, ‘door’ is marked in green, ‘walls’ are marked in dark green, ‘floor’ is marked in blue, etc. The accuracy of classification of objects is around 93.5% comparable to human accuracy. The objects to be recognized can be chosen for each application simply by changing the training database.
Figure 6: AI Automatic 3D object recognition. Plan view of original point cloud data for a conference room and 3D recognised objects. The ceiling was removed for clarity in viewing the inside of the room.
Artificial intelligence in the cloud As the AEC sector embraces digital technology, the amount of data produced grows exponentially, creating large data repositories. To generate useful and actionable information from this ‘big data’ requires leveraging smart analytical tools such as AI that are becoming more accessible, especially when hosted from the cloud. Both the cloud computing infrastructure and artificial intelligence supply the tools to leverage and enable digital technology by providing convenient methods of working at scale, thus lowering the barriers to entry for users to these new ways of working. Artificial intelligence (AI) neural network and deep learning require vast databases of thousands of examples for training, which can be conveniently stored in elastic expandable cloud storage on demand. AI software requires highly parallel processing on many parallel processors to carry out the training in a reasonable time, again easily available in cloud computing infrastructures.
Intelligent combination and use of available techniques such as laser scanning, automatic alignment, cloud computing and artificial intelligence can not only speed up analysis of vast data sets but also improve accuracy and release human activity to ensure that a product is correct and useful.
BENEFITS OF USING THE CLOUD Auto-Application Updates: applications are updated automatically, so the user always has access to the most up-to-date optimised software and bug fixes. Responsivity: dedicated development support teams continuously monitor user experience to optimise and, if necessary, rewrite code. Scalability, flexibility and agility: Scalable elastic cloud environments on pools of servers, storage and networking resources scale up and down according to the number of users and the volume of their usage. They automatically scale up and down as users’ needs change. Capital expenditure free: users have access to the highest power computers. There is efficient use of hardware as users do not need to purchase, manage and maintain large amounts of computer and storage hardware, resulting in lower hardware, power, cooling and IT management costs. Users only pay for what they use as the cloud resources automatically scale, so it is easier for small businesses to manage their business at any time of day, from anywhere. High speed: multiple computers run in parallel so many different parts of the same point cloud can be processed at the same time and many different users have no effect on speed or quality. Security: the data is stored and communicated securely with a level of encryption chosen by the user. If security is a paramount concern, the software can run on a private cloud without internet connections in-house. Clouds can be configured to make use of certain data centres, such as within one country if intercountry security is a concern.
Availability: if one server is busy or not available then another server takes its place to provide full availability. Disaster Recovery: data is stored in multiple locations at the same time so if storage hardware in one data centre breaks down, the calculation proceeds with little interruption as the data is backed up elsewhere. Data archiving facilities are automatically provided. Latency: if latency is important, the cloud can be configured so that local clouds provide low latency to the user. Increased collaboration: many users, located globally, and mobile users, can store, process, share and view datasets at the same time without any loss of speed or responsivity. Reliability: the application software can make use of resources on cloud computing infrastructure provided by different vendors in different global regions. Forward compatible: an open cloud architecture is forward compatible to match higher power computing resources as they are rolled out. Sharing: all point cloud datasets are secure in one place and accessible at any time from
Author: David Selviah
Last updated: 04/08/2020
Drone Survey What Is Involved
Drone survey is the use of drone technology to combat a variety of maintenance solutions around assets and infrastructure in any industry that might require regular or dynamic maintenance techniques. Drone survey is the current most disruptive innovation we have seen in recent years and although drones and their very nature are difficult to comprehend by many, they are essential for any industry wanting to move forward.
Drone survey can embellish your current maintenance program by providing essential data that might normally only be available during shutdown periods and prolonged outages of specific areas.
Safety Improvement
Drone survey in the first instance is essentially the safest method of surveying assets and infrastructure currently available. There is no requirement for a manned element during any part of the survey, at height or in arduous areas that may be deemed potentially dangerous for manned inspection techniques.
When assessing risk, the first mitigation is always how can we achieve the same results without the inherent danger to personnel. Drone survey (s) are now able to combat this issue.
Drone Survey Vs Traditional Survey
Cost Effectiveness
Due to the nature of drone survey it eliminates the requirement for prolonged access methods such as scaffold, ladders, lengthy MEWP hire contracts, Teams of rope access personnel or cranes with work baskets. Generally, all that is required is a UAV pilot and an observer (dependent on the project undertaken).
This alone reduces the costs significantly and although a higher day rate will no doubt be obvious, the time taken in comparison to anything else on the market will be significantly reduced by up to weeks (dependent on project) This combined with improved safety is a winning combination of innovation and disruptive technology.
Improved Data Acquisition
Drone survey technology can provide superior outputs via a live feed to a designated ground station, providing hi-resolution visual evidence to the project team if required or recorded directly to the onboard memory and uploaded and analysed at a later date. A report can be established pinpointing any remedial requirements giving the project team an accurate analysis and targeted area to plan any corrective actions, without the need for a wrap around access solution such as scaffold or team of rope access technicians.
The data collated during the drone survey can be used for years to come creating a formidable trend analysis providing data that can be utilised to analyse, erosion, defect, deterioration, Corrective and planned maintenance and overlaid to compare with previous years. Allowing project team to better assign budgets and plan outage or maintenance periods.
Time Efficiency
Time taken to complete visual, geographic or standard structural surveys can be extremely time consuming with the rising costs of traditional access methods which would generally require teams of qualified men to either erect and operate said equipment. Can run into weeks if not months. For instance, to erect a wrap-around solution such as a scaffold to carry out an inspection of the upper elevation of a building may run into weeks prior to a qualified assessor providing their services on the condition by scaling the scaffold by hand which again could run on. Drone survey is the only solution that combats the traditional time-consuming issue.
Again relating to the above point the data collated by an assessor is visual but the quantity and quality of that data collection are not as effective as when acquired adopting drone survey resulting in hugely improved time scales which is the main contributing factor to cost efficiency.
Improved Logistics
Drone survey and the services that encompass it can be carried out with minimal disruption to the surrounding environment. Drastically improving logistics.
Road closures may be required when using traditional methods,
Prolonged shut down of certain elements to allow the access method to remain for the required period.
Traffic delays due to the offload of equipment such as a scaffold or access platforms
The potential for dropped objects into the public right of way
The potential for vandalism, allowing unqualified personnel to access scaffold or MEWP causing damage and potentially harming people
The potential for falls from height – Remote locations preventing use of traditional methods
There are certainly several issues around traditional methods and their work arounds which can be mitigated out utilising drone survey as a replacement or additional solution. There are obviously certain elements that may require consideration when using drones also but logistically allot easier to manage.