The use of drones to map disaster areas provide more significant advantages in costs and in rapid response times when compared to traditional methods. They generate high-resolution and 3D mapping, help in identifying hotspot areas that have sustained the most damage, and upload the data in real-time to coordinate relief efforts.

India Flying Labs (IFL) is the India chapter of the Flying Labs network, backed by WeRobotics (currently active in more than 25 countries). IFL is an open, collaborative network of individuals and organizations that came together to apply robotics, drones, and data for useful social applications such as conservation, disasters, healthcare, agriculture, and sustainable development. Their work is based on the Humanitarian UAV Code of Conduct.

Dr. Ruchi Saxena, Founder of Caerobotics Healthcare Solutions and Director of India Flying Labs, is actively engaged in creating an ecosystem for utilization of unmanned robotics in healthcare and disaster management in India, Nepal, and other Asia Pacific countries. She prefers empowering local teams with the knowledge, technologies, and opportunities so that they can solve their local challenges.

Dr. Ruchi has an exciting story to share regarding one of their initiatives in which the Nagaland State Disaster Management Authority (NSDMA) and India Flying Labs built local drone data expertise for quicker disaster response.

Fig: High-resolution map (ortho) of village Meriema, Dimapur, India

Every year Nagaland receives very heavy rainfall, which has resulted in several natural calamities all over the state. Landslides and sinking zones have created immense damage to public infrastructure all over the state as they also caused the loss of life and property.

To mitigate these challenges state government seeks the help of the central government in the relief and rescue operations, distribution of essential relief material and to provide medical support to affected people. In times of emergency, delay in such activities risks the life of an individual.

Hence, NSDMA realized the need for local community support in making Nagaland resilient to disasters. They stressed the need to make a “Family Disaster Management Plan” so that every family in Nagaland is prepared for any disaster event.

To build the UAV Rapid Response Task Force for Disasters, NSDMA called up India Flying Labs to educate localites as well as their staff to understand on “How to use drone data for emergency response?”.

In response to this IFL has conducted a four-day ‘Drone Data Expert Workshop’ for 36 enthusiastic localites and disaster management professionals. The motive of the workshop was to introduce various applications of drone data in search and rescue, identification of land-sinking areas, landslides monitoring, earthquake preparation as well as enhance the decision making of government officials during a disaster emergency.

Fig: Glimpse of Drone Data Expert Workshop

The four-day workshop was divided into :
1. 1st day of the workshop started with the ‘World of Drones’ session, where IFL taught the fundamentals of flight using paper airplanes. This fun and familiar activity helped the participants understand how to operate a drone and how to employ them as data collection tools.

2. On the very next day, team IFL explained the basics of mapping and how to use Google Earth for planning drone missions. They also introduced participants to Picterra, an AI-based tool that simplifies counting elements in an image quickly. The teams then had a role-playing exercise to plan for the drone flights and learned about drone safety checklists and team coordination protocols.

3. The next day was flight day. After a week-long of fog and rain from Cyclone Bulbula, the teams greeted the bright sunny day ready to practice what they learned over the previous two days. Teams came back with the aerial data from the village Meriema and had a hands-on with experience with Pix4D and Drone Deploy for data processing. Keeping in mind that the rescue team often works in limited internet connectivity zones during a disaster, leads processing of drone data as a challenge. To eliminate this hurdle, they have introduced pix4D react to participants in which they can process the data offline and that too very fast.

4. Last but not least the teams had a live session with the Indshine team, where they learned about using Indshine for project collaboration, analysis, reporting, and sharing. Finally, they used Picterra for counting the houses, and by now, the team handled Picterra like pros! They went back to Google Earth to view the aerial maps and made a 3D walkthrough of the village. They also explored the use of Survae for viewing drone videos and then Open Aerial Maps and Open Street Maps.

This helped in making participants familiar with analyzing drone images & videos and using them for “before, during & after” emergencies. They used the Indshine tool to strengthen their collaboration with government officials and local teams even further. It is allowing officials to work remotely on the drone data and annotate points of interest like houses, hospitals, roads, open areas, etc.

Click on the above Nagaland state disaster management project to get hands-on experience.

Dr. Ruchi explains: Indshine is promoting the development of the cooperative ecosystem in the drone industry.

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Pix4D React meets Indshine || Drones in Disaster Management was originally published in Indshine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Today, we are introducing, “Servicios de Ingeniería”, a technology firm based in Mexico, specialized in providing topography, mapping, and engineering services to the Construction, Mining, and Oil & Gas Industry. They are also using drones and satellite imageries to map their client’s assets and to carry out elevation profiling and volume estimation based works.

Video: Aurelio Acosta story.

Aurelio Acosta, founder of “Servicios de Ingeniería”, has 20 years of experience in the field of topo-surveys, GPS and GNSS technologies. He is an expert in using these technologies for various use-cases in the field of mapping and topography. He has recently started working in the field of ​​Photogrammetry and LiDAR since 2015.

He has an interesting story regarding one of his projects — for laying of gas pipelines on “VHC Gas Station” in Cabo San Lucas region. This is a classic example of how the combination of “orthomosaic and satellite imageries overlayed with AutoCAD files” can help architects/engineering teams that are sitting in different corners of the world in creating structural designs.

VHC group was looking for hand-holding for their planning and monitoring of industrial area on 17.51 acres of land.

Fig: Layout for Gas Station, warehouse, and concrete plants

“Servicios de Ingeniería” came to the rescue of the VHC group.

The first task was to highlight official limits/boundaries of ownership in GIS-based software, followed by tracing the same on-field using GPS/GNSS equipment.

Post tracing of boundaries, 3D laser technology was used to figure out elevation values of the entire area of interest. This information was sufficient to plan out major activities of work including placement of Gas Station, Building, etc.

The next task was to constantly update the progress of the project, which was carried out using drone-based maps/ orthomosaics.

Report: Drone Image Processing Reports.

“Servicios de Ingeniería” was facing two major problems:

1. Preparation of Reports:

In construction projects, the elaboration of progress reports has always required a lot of effort. It is difficult to generate actionable insights from drone mapping, resulting in a delay in the decision-making process and in turn, leading to an increase in the overall cost of the project.

2. Client, Consultants, and Architects in different locations:

While the owner of the company was operating from Spain, half of their architecture team was in Montana USA, another half in L A, USA and consultant was in Mexico. There is a need for an effective solution by which they can act as a single source of truth for the whole team.

Indshine offers “Servicios de Ingeniería” a simple and intuitive digital platform where the client, consultants and engineering team could access the engineering drawings from their respective locations and observe the progress of work in real-time.

Now, it is much easier for all stakeholders to access drone and satellite-based high-resolution maps including orthomosaics and digital elevation models. Moreover, the option of overlaying AutoCAD files is cherry on the cake.

Click on the above VHC Gas Station project to get hands-on experience.

Acosta explains - Indshine has the feature of adding collaborators in the project so that they contribute their observations, make modifications and add information, this feeds the project and is given an efficient and real-time follow-up, helping to take decisions in a faster and more efficient way, reducing the time and total costs of the project.

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Owner in Spain, Consultant in Mexico and Architect in USA|| Drones in Oil & Gas was originally published in Indshine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Is Construction project monitoring a challenge for engineering firms?Construction lenders often require their team on the ground to report on the progress of a project. With the intervention of drones in the field of Construction Progress Monitoring, firms will now have access to construction progress monitoring reports. The sites will be monitored regularly to ensure that the activities are being performed as per the terms and conditions mentioned in the contract. Drones will now allow monitoring construction progress from your office resulting in mitigating risk and managing construction sites with high precision.

We are introducing one such company, GM6 solutions, which specialises in providing drone-based solutions for construction, mining, oil and gas sectors in Texas, USA.

Troy Willmon, Founder of GM6 solutions, began his career in law enforcement over 15 years ago. While serving in his capacity as a police officer, he was appointed to the position of Director of Emergency Management. Troy introduced GIS into the emergency disaster plan as well as the E911 system, which shortened the response times and saved many lives. Troy has been passionate about using geospatial data to improve safety and industry standards which led to the foundation of GM6 solutions.

GM6 solutions have two offerings for their clients. The first one is photogrammetry/mapping services, which they provide to land developers, Engineering firms, Oil & Gas, Construction companies and Public Safety sector. They are using drones to map their client’s assets so that they can generate a detailed project report and bring out accurate measurements from that data.

Apart from this, the second one is, providing drone training services to companies and freelancers for drone data collection and using photogrammetric software to process, visualise and share those datasets.

Troy has an interesting story to share regarding one of his project on measuring progress in their on-site construction projects.

GM6 solutions has been working on a construction project of 64,000 sq. feet building being built on 17 acres of land. They have used three different models of drones i.e., Phantom 3 Pro, Phantom 4 Pro, and a Mavic Pro with a varying flying height ranging from 30 to 121m, to test out different Ground Sampling Distances.

The report attached below beautifully talks about the accuracy of the results and Ground Sampling Distance with various flying (height, instruments, sensors).

Troy mentions that one of the prominent challenges faced by the industry is construction site progress monitoring. There is a lack of transparency in regards to what is happening on the construction site.

Since Troy is already enriching their clients with orthomosaic (high-resolution maps) — digital information in anticipation of increasing transparency but they are finding it difficult to absorb the information because of prerequisite know of GIS environment. Also, as the datasets are very large in size, they are difficult to handle and hence require complex hardware.

The clients have specific requirements of knowing the progress across the timeline to help them understand the progress across the timeline in one go. GM6 solutions took this as a challenge to present insights from the 2D and 3D drone maps.

Troy was looking for an effective and economical solution that can allow his clients to utilise the drone datasets in a much simpler manner and bring analytics out of it.

Indshine’s cloud platform helped GM6 solutions by giving them the ability to upload all the raster files in one project and giving them an option to compare high-resolution maps of different timelines in the form of layers through time-lapse orthomosaic. In addition to this platform provides a toolkit to measure the elevation profiles and a feature to mark the relevant Area of Interest, including playgrounds, buildings.

Layers Panel highlights raster files and Annotation panel — the vector files.

Troy Explains- Indshine to me is Drone Deploy meets GIS

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GM6 Solutions Success Story was originally published in Indshine on Medium, where people are continuing the conversation by highlighting and responding to this story.

One such great stepping-stone has been initiated by Boris Fernández Astro, Geographer Engineer. He graduated from “EMI” (Escuela Militar de Ingeniería, La Paz — Bolivia) in 2014, and has been working in the field of ​​Photogrammetry and LiDAR since 2015 in “SNA” (National Aerial Photogrammetry Service).

Boris has presented research projects on processing data from drones, airborne photogrammetric cameras, new technologies, and methodologies to transform raw data to value-added products as well as presenting them with proper visualization tools.

He is an independent consultant in the field of geomatics and is a promoter of the Astro Geomatica with an experience of over 3 years in academic research in the field of geomatics (astrogeomatics.com — Facebook: @astrogeomatics). Astro’s objectives are to impart knowledge and provide specialized services in academics and research in the field of Geomatics and photogrammetry.

Nowadays, he is trying to find a professional way to offer photogrammetric services at a distance, thereby providing an option for the client to have the required deliverables anywhere, anytime, and from any device.

Cloud computing is what’s exciting Boris to deliver both personal as well as virtual presentations to students and the public directly in real-time. It is contributing heavily in the field of photogrammetry, which aims at either presenting photogrammetric results or improving the way of interacting with them through a visualization mode. He has shared his tutorial on the topic.

Boris is using Indshine platform for easy visualization of raster and vector files.It allows him to go for vectorization as well as facilitates him to circulate demos of projects and his work with potential clients. All of this with various levels of access to data and without the concern of infringing their confidentiality.

The best part he feels is that it is not mandatory for all its users or potential customers to have GIS software and heavy workstation, to have their information in hand.

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GIS meets Cloud Computing || Astro Geomatica Success Story was originally published in Indshine on Medium, where people are continuing the conversation by highlighting and responding to this story.

The precision agriculture market has been growing at a rapid pace lately. Technologies (such as VRT, remote sensing, GPS, GIS and guidance technology) deployed by precision farming increase productivity, decrease labor overheads and enable optimal use of resources.

We are introducing one of the companies working in Agri-Tech in Brazil, South America — Velbrax Agro (https://www.velbrax.com), which specializes in providing drone-based solutions to organizations producing sugarcane and orange crops.

Carlos Orion, co-founder at Velbrax Agro using a fixed-wing drone, with an RGB sensor Sony A6000 (24MP) mounted on the drone, captures images according to the grid flight plan and then process those images in photogrammetry software to create high-resolution orthomosaic. He has in-built algorithms that highlight crop-failing areas in crops like sugarcane, oranges, etc. by locating the distance between two seedlings. Interestingly, these distances on orthomosaics can give a quick idea to anyone regarding the crop failure percentages. Attached screenshot of the report.

Fig: % failure and distances between seedlings

(Note: relatório de paralelismo == parallelism report ; intervalo de espaçamentos==spacing range ; % de espaçamentos== % spacing; eixo vertical valor==vertical axis value)

The results are marvelous since Velbrax Agro is able to highlight crop failing of 6.95% of soybean production in 250 hectares of land. Take a glimpse of his work at Indshine.

Now, the major issue for Velbrax agro and organization was to act upon their results and sort out the potential reasons for the crop failing.

Key stakeholders from the organization find it very easier to use maps (RGB, NDVI) on Indshine platform while on field scouting, plus the markers and annotations which Carlos has built-in are easily being superimposed on maps. Stakeholders are enjoying the collaborative environment by comments and making notes for actionable for relevant authority. Since the platform has easy sharing tools, Carlos finds it easy to share these with no hassle.

Fig: Sharing Data online

Indshine is the best way to show their works to their existing as well as prospective clients. Indshine is a new concept of mapping share. You can choose how many and how people have access.

Carlos Orion, co-founder at Velbrax Agro

In a nutshell, the drone data, which used to pile up in the personal computer’s have now got a medium to be properly utilized. Carlos is happy that his client can now easily work on drone maps, helping him to earn more projects.

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Drone Mapping meets GIS || Velbrax Agro Success Story was originally published in Indshine on Medium, where people are continuing the conversation by highlighting and responding to this story.

The above figure determine the images of object at points A and B; is considered as the part of the overlapping area within the two successive imagery vertical aerial images taken from the camera at the position in the space. Camera focus on point parallel to the flight line. In the side image they appear as a’ and b’. Point A movement is greater because it occurs at higher position than that of the point B. The stereoscopic parallax at A and B are parallax a(Pa) and parallax b(Pb).

Pa = Xa-Xb’

This is consider as the x direction parallax, similarly, to the x, y is also calculated to the relative position of the object in the imagery. As explained in the second figure Xa and Xa’ are the measuring photo coordinates of object A an the left and right respectively. These coordinates are not from the fiduciary system but are based on the flight direction of point X and X’ for measuring the parallax. It can be along the flight line for every imagery which is captured during the data acquisition of a stereoscopic pair.

What is Parallax in Photogrammetry? was originally published in Indshine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Just to give you the hint of the series that I’m working on; I’ve covered the core theoretical concepts of image processing. This article is in continuation of the same. Let’s start with defining Absolute Orientation and why it is a crucial part of image processing?

What is Absolute Orientation?

Absolute Orientation introduces the relationship between image coordinate and the corresponding ground coordinates. In this process, the model is scaled and leveled as per the reference datum. This method is used to mark local points in reference to the global points. Each pixel in the imagery is scaled and leveled in such a manner that it prepares a model for the DEM which supports required GSD of the orthomosaic in measuring the various features in the imagery.

Absolute Orientation is understood by the following two processes:

1. Scaling the Model

Scaling the model can be understood as scaling the photo/imagery to the original distance on the ground. The model is based on Relative Orientation model. If the X and Y setting of the two projections are equal then the model base is composed only of an X component called bx (image coordinates) and model scale is varied by simply changing the model base by the change in bx.

A minimum of two horizontal control point is required to scale a stereo model. These points are plotted in the adopted model scale on the manuscript map. The manuscript is then positioned under the model and with the floating mark set on one model. It is rotated until it maps the same line that is collinear with the model line on the imagery. The model scale is too small and must be increased by increasing the model base until the line on the model is equal in length to the line on the manuscript map. If the Y and Z setting of the projections are not equal then projectors will have to be moved appropriately.

2. Leveling the Model

This procedure requires a minimum of the 3 vertical control points distributed in the model so that they form a large triangle. A model with vertical control points near each corner that has not been leveled. To stable the model generally four points are preferred, each marked near to the corner in the model, and a fifth point near the center of the model is also desirable. Note that there are two components of tilt in the model. The amount by which the model is out of level, each of these components are determined by reading model elevations of the vertical control point and compose them with their known values.

For correcting X and Y the projector bar is tilted to distribute the relative orientation error.

Thank you, for reading my article. Please, follow the publication for updates. Also, benefit from the shared public projects on Indshine.

Part 4: Theoretical Core of Image Processing that Interprets Drone Imagery was originally published in Indshine on Medium, where people are continuing the conversation by highlighting and responding to this story.

In my previous articles, I’ve covered Interior orientation, Exterior orientation and in the continuation of the same I am writing this article on Relative Orientation.

Relative Orientation

This concept required the presence of two or more overlapping pair of aerial photographs. By a process called relative orientation, the two projectors on top of the two overlapping photographs are oriented so that it bears the exact angular orientation to one another as in the case when they were exposed.

Relative Orientation creates a miniature true 3D stereo model of the overlapped area. It determines the relationship between two images forming a model. At the instant when two photographs are taken from a stereo pair there will be an altitude and position relation between two photographs. Corresponding points in the images taken from two different cameras; can be used to determine the object in the scene, one has to determine the position and orientation of one camera with respect to another.

Along the flight direction consider 60% overlap and 20-30% overlap in lateral direction as per the standards of the photogrammetry. This percentage can be increased up to 80% for both the overlap to achieve accuracy.This is the classic photogrammetric problem of relative Orientation.

The internal geometry of the cameras, including its principal distance and the location of the principal point, ray can be constructed by connecting the points in the images to their corresponding projection center. These rays, when extended, intersect at the point in the scene that gives rise to the image points. This is used to determine the position of points in the environment. The two projectors are first positioned are in relative orientation to one another by estimation. Then using the tools of the stereo plotting instruments the various orientations are carried out to align the two imagery.

Thank you, for reading my article. Please, follow the publication for updates. Also, benefit from the shared public projects on Indshine that you don’t have to log in to see and contribute to get yourself noticed among industry people.

Part 3: Theoretical Core of Image Processing that Interprets Drone Imagery was originally published in Indshine on Medium, where people are continuing the conversation by highlighting and responding to this story.

In the 1st blog of the series, I wrote about Interior Orientation (Check it out, if you haven’t read it) which is the internal geometry of a camera or sensor as it existed at the time of data capture. Now, I will put down my theoretical knowledge on Exterior Orientation which is another part of the image processing.

Exterior Orientation

Exterior Orientation is the position and angular orientation associated with a drone image.

Elements of Exterior Orientation

The variables defining the position and orientation of an image are referred to as the elements of exterior orientation. They define the characteristics associated with an image at the time of exposure or capture.

1. The positional elements of exterior Orientation include Xo, Yo and Zo. The coordinates define the position of the perspective center with respect to the ground space coordinate system X, Y and Z. Zo is commonly referred to as the height of the camera above the sea level which is commonly defined as “datum”.
2. Angular or rotational element of exterior orientation describes the relationship between the ground space coordinate system (X,Y and Z) and the image space coordinate system (x,y and z). Three rotation angle is commonly used to define angular orientation. They are Omega, Phi and Kappa.

Omega is a rotation about the photographic x-axis, Phi is a rotation about the photographic y-axis and Kappa is a rotation about the photographic z-axis, they are defined as being positive if they are counterclockwise when viewed from the position end of their respective axis. The photographic z-axis is equivalent to the optical axis (focal length).

The x’, y’ and z’ coordinate are parallel to the ground space coordinate system. Using the three rotation angle, the relationship between the image space coordinate system (x,y and z) and ground space coordinate system (X,Y and Z) can be determined. Relationship between the two systems can be made using a matrix and is referred as orientation or rotation matrix.

Thank you, for reading my article. Please, follow the publication for updates. Also, benefit from the shared public projects on Indshine that you don’t have to log in to see and contribute to get yourself noticed among industry people

Part 2: Theoretical Core of Image Processing that Interprets Drone Imagery was originally published in Indshine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Interior Orientation

It is the internal geometry of a camera or sensor as it existed at the time of data capture. It is primarily used to transform the image pixel coordinate system or other image coordinate measurement system to the image space coordinate system(local pixel position to the global pix position system). It is the relation between the pixel coordinate system (x0,y0) and the image coordinate system(X, Y).

Internal Orientation is set up by measuring the fiducial mark. Data from the camera calibration is used to set up the camera file for the interior orientation. Fiducial marks are measured manually. The purpose of interior orientation is to establish a relationship between pixel coordinate system and the image coordinate system. The relation between pixel and image coordinates is almost constant. Fiducial mark is registered onto the film by projecting an image of the mark through a small lens.

Coordinate system transformation is the most elementary operation. From interior orientation to relative orientation, to absolute transformation is based on the coordinate system transformation. They are uniform, scale, rotation, reflection, stretch(non-uniform scale factors), etc. Coordinates are computed by 2D transformation, whose parameters are estimated using the measured and calibrated position of the fiducial marks.

The internal geometry is defined via terms:

Principal point

Focal length

Fiducial marks

Lens distortion

Principal Points

Mathematically it is defined as the intersection of the perpendicular line through the perspective center of the image plane. It is the image position where the optical axis intersects the image plane. This is calibrated in two forms(to see the camera calibration report).

1. Principal point of autocollimation: The principal point is the principal point of autocollimation (PPA). It is defined as the image position where the optical axis intersects the image plane.
2. Principal point of symmetry: The principal point of symmetry (POS) is also known as the calibrated principal point. It is the point on the image where a ray of light traveling perpendicular to the image plane passes through the focal point of the lens and intersects the film.
3. The principal point of symmetry is used to compensate for the lens distortion.

Focal Length

The length from the principal point to the perspective center is called the focal length. The plane is commonly referred to as the focal plane. (Add the camera details) Prior to conducting any photogrammetry project (drone flight), the focal length is calibrated accurately.

Fiducial Marks

It involves determining the image position of the principal point for each image in the project. The image position of the fiducial marks are measured on the image and subsequently compared to the calibrated coordinates of each fiducial mark. The measured image coordinates of the fiducial marks are referenced to pixel or file coordinate system. The pixel coordinated system has an x coordinate (column) and y coordinate (row). The origin of the pixel coordinate system is the upper left corner of the image.

The relationship between the pixel coordinate system and the image coordinate system is defined are as follows:

The coefficients required to transform pixel coordinate measurement to the image coordinates ie x and y image coordinate associated with the calibrated fiducial marks and the X and Y pixel coordinate of the measured fiducial marks are used to determine six affine transformation coefficients.

x = a1 + a2X + a3Y

y = b1 + b2X + b3Y

The quality of the two-dimensional affine transformation is represented using a root mean square error (RMSE) indicates poor correspondence. This can be attributed to film deformation, poor scanning quality, out-of-date calibration information or image mismeasurement.

Lens Distortion

The position accuracy of the image points located on the image plane; the light rays passing through the lens are bent, thereby changing direction and intersecting the image plane at positions deviant from the normal. Lens distortion value is provided as a function of radial distance from the principal point or field angle.

1. Radial Lens distortion ⃤ r : Radial lens distortion causes images points to be distorted along radial lines from the principal points(it is also known as symmetric lens distortion).
2. Tangential Lens distortion ⃤ t : Distortion occurs at right angle to the radial line from the principal point.

Thank you, for reading the article. In my next article, I'll cover External Orientation which is another theoretical core part of image processing. Follow the publication for the updates. Also, benefit from the shared public projects on Indshine that you don’t have to log in to see and contribute to get yourself noticed among industry people

Part 1: Theoretical Core of Image Processing that Interprets Drone Imagery was originally published in Indshine on Medium, where people are continuing the conversation by highlighting and responding to this story.