Some of Today’s Most Profitable Quadcopter Drone Uses

Drones, in particular, quadcopters, are quickly becoming the standard way to obtain high-quality images and other data for areas that have been hard or expensive to access. There are many quadcopter on the market, and their capabilities are unfolding rapidly, and companies such as WingsLand offer a wide array of capabilities, ranging from a mini-quadcopter that can fold up and fit in one's pocket, to larger drones capable of longer flight times (check out Drones for Sale).

Small drone services providers maintain at least four drones to assure there are sufficient back-ups and also redundancy in order to cover more than one job at the same time.  Technology is changing so quickly that it’s a good idea to have a plan for quick payout of the drone (along with the cameras and sensors), along with software licenses and cloud-based storage so that you can plan to upgrade your equipment and maintain a high-quality product.

It is important to keep in mind that FAA regulations still involve a number of restrictions, and it is illegal to fly near airports, over stadiums, and in cities. There are also a number of privacy and security issues which must be considered when developing the flight plan and workflow.

You also need to have a good idea of the flight time for your drone. Which are the drones that have the longest flight time, and what payload can they carry? You must investigate this aspect very carefully. Here is a link to the longest flight time drone and others.

Photography and 3D Imaging

Real Estate:  Drone photography is used in many ways in real estate, including surveys, sales and in evaluation of projects.  In addition, drone photography is used in conducting inspections of the structures as well as the grounds.
Sales and Surveys
High-end residential
Commercial buildings, especially for planning renovations
Reconnaissance / Opportunities assessment, especially in rural or coastal areas
Acreage / ranches / development
Inspections
Roofs
Commercial buildings: façade, windows, roof

DJI Phantom 5:  Built-in camera, easy to get started.

Events:  Drones are often used at weddings, festivals, sporting events, but the usage must be carefully planned in order to avoid legal issues. First and foremost are safety and privacy issues.  It is important to obtain signed releases from the people who will be photographed.
Weddings
Festivals

Natural Disasters:  Drones are extremely useful in determining the scope and impact of a natural disaster, and can be extremely helpful in identifying impassible infrastructure. They are also used in search and rescue operations, and can help identify the direction of quickly moving wildfires. Drones used are often equipped with infrared / FLIR sensors as well as high-resolution cameras.

Inspections: Equipment / Operations:  In addition to real estate inspections, drones are being used to inspect inaccessible locations and ones requiring 3D visualization.
Insurance companies: Buildings, infrastructure
Bridges
Solar panel inspections
Wind turbine inspections
Pipelines

Infrared / FLIR / Multispectral Sensors

In addition to high-resolution photography, using sensors that allow you to detect thermal variations can help you generate false color composites from which you can extract a great deal of very useful information. Here are some of the most popular uses. It is usually a good idea to combine multispectral images with photogrammetry. 

The FLIR Duo is a new product that combines thermal and visible light imaging and has been designed for drones.  FLIR is a leader in thermal imaging, both for professional and personal use. 

Watch video: https://www.youtube.com/watch?v=8UCcyZboM9k

• Agricultural: Precision agriculture; monitoring crop health and irrigation needs.
• Security and Surveillance:  These sensors pick up heat sources, which include bodies – human, animal, or otherwise.
• Environmental:  Determining coastal erosion, chemical spills, the depth of water bodies, and distressed vegetation.    
• Herd Tracking (Commercial and Feral): Have a feral hog problem? Are deer eating your favorite bushes? Drones, combined with fixed surveillance cameras / sensors can help you identify the nature of your intruders.  
• Leak detection in buildings: Thermal signatures are used to pinpoint leaks in roofs and other structures.
• Hydrothermal resources / hot springs (and possible affiliated mineralization): Thermal anomalies are used in identifying geothermal resources and also places with possible mineralization due to the action of hot, mineral-saturated waters. 
• Search and Rescue:  Heat signatures can help identify individuals needing to be rescued, especially at night. 

Fugitive Gas Emissions / Hyperspectral
Hyperspectral and multispectral sensors are being used to detect fugitive methane from operations and also natural gas seeps. The process is used for methane leak detection as well as identifying possible areas where oil and gas may be found in commercial quantities.

The Future
As the equipment and sensors improve, the cloud-based 3D imaging will also improve. The spoils are for the innovative, creative, and those who clearly identify the problems that are best solved by means of drones. Of course, drones will generate their own unintended issues -- but a problem is always another opportunity.

Data Mining: What You Might Not Know

The ultimate goal of data mining is not the acquisition of data, but the exploration and analysis  of massive amounts of data resulting in patterns, rules, and relationships. One of the key outcomes is the identification of reliable and meaningful patterns.

Meaningful patterns can do the following:

* Model typical behaviors
* Identify atypical behaviors
* Express possible cause and effect relationships
* Explain past behaviors
* Describe current conditions
* Develop a predictive model for the future

While developing patterns in data drawn from different types of data can show meaningful  relationships, time-series data mining can be used to postulate

* causality
* life-cycle behaviors
* impacts of proximal or distal relationships
* cluster formation and disaggregation

Characteristics of Data Mining Data

The data may represent changes of behavior and activities over time, or, alternatively, it could  represent the relationship of different types of data which have been collected at one point in  time.

* Same type of data, collected at different points in time
* Different types of data, collected at the same point in time
* Data streams, which involve ordered sequences of items that arrive over time

* offline: regular chunked arrivals
* online: continuous flow

Planning the Data Mining Process

The development of data mining can follow a fairly clear process:

1.  Determine the problem and definition
2.  Determine the characteristics of the data
3.  Develop a plan for data mining
4.  Review of similar data mining projects and algorithms
5.  Become familiar with data, data issues, potentially meaningful data subsets
6.  Data preparation and conditioning
7.  Model and algorithm development
8.  Evaluation of model / comparison with other models
9.  Implementation, which involves generating reports, or continuing to develop ongoing activities


Data Mining: Key Tasks

Key tasks in data mining include the following:

* Identification
* Eliminate unnecessary or distracting frequent item sets
* Definition and differentiation
* Optimize storage and recovery of streams
* Classification
* Cluster recognition
* Segmentation
* Discovery of motifs (sub-sequences)
* Detection of similar clusters or sets
* Detection of outliers and anomalies
* Create predictive models

Data mining that involves continuous streams of data presents unique challenges because of the  nature of the data and types of patterns that are meaningful, given the array of patterns that are  possible to develop. It is also challenging to integrate incoming data with existing databases in  order to qualitatively evaluate patterns in a timely way. It is also challenging to avoid the  "concept drifting" problem, which means that the usefulness and validity of the results will  degrade over time.

In general,

* Sensors and surveillance: networks, physical locations, manufacturing, transportation
* Performance monitoring: manufacturing, networks, controls
* Transaction / activity monitoring: retail, web performance, manufacturing

Algorithms

A literature review of algorithms suggests that data mining for data streams is generally  performed using three different major classifications of algorithms, and that they do not yield  the same results, which could be quite significant, depending on the application.

Landmark Window Based Data Mining

What is measured is the difference between a specific time-stamp (the landmark) and the present.

Pros: Complete comparision with an a priori property
Cons: The order in which information is considered and placed into sets can lead to errors

Damped Window

This approach privileges new data over old or historical data, which means that the older data drops out of  consideration for developing sets

Pros: Efficient use of resources, eliminates old and obsolete information
Cons: Large errors may be made because the information being eliminated may be important for the  rule to be effective

Sliding Window

Sliding favors new data (as in the Damped Window approaches), but does not completely eliminate  old data. Instead, it incorporates summarized versions of old data and data relations.

Pros:  Can incorporate past data and do so relatively quickly
Cons:  The assumptions made to create summaries of old data sets can be flawed

General Observations and Conclusions

At this point in time, the ability to collect data continues to expand and sometimes dramatically,  thanks to technological advances in both hardware and software. However, a review of the processes  and the literature make it clear that the algorithms use to process and make meaning of the data  batchs and streams differ widely. Consequently, the results and conclusions that are created using  data mining techniques (both collecting and in analyzing), can be highly variable. Thus, decisions  made through data mining need to be made carefully, and more than one analytical technique and set  of algorithms should be used.


References

Esling, P., & Agon, C. (2012). Time-Series Data Mining. ACM Computing Surveys, 45(1), 12:1-12:34.

Mala, A. A., & Dhanaseelan, F. (2011). Data Stream Mining Algorithms: A Review of Issues and  Existing Approaches. International Journal On Computer Science & Engineering, 3(7), 2726-2732.

Ramageri, B. M., & Desai, B. L. (2013). Role of data mining in retail sector. International  Journal On Computer Science & Engineering, 5(1), 47-50.