Given the vast amount of information available on LiDAR, including its types, applications, and standards, a comprehensive resource would be helpful for civil engineering professionals seeking to leverage LiDAR in their projects.   

The new White Paper, “LiDAR Technology for Civil Engineering,” is a promising solution, offering an A-to-Z guide on LiDAR’s relevance and use within the construction industry.  

Download now! It’s free. 

Knowledge at a Glance: What to Expect?   

LiDAR (Light Detection and Ranging) technology captures precise, three-dimensional information about physical environments. It advances critical tasks in surveying, planning, monitoring, and managing, helping to build and maintain the essential services and infrastructure that make modern life possible.    

The impact spans all civil engineering project stages: design & planning, preservation and retrofitting, risk management, environmental and land use, asset management, etc.  

Read also: ML-Powered Data Processing for Civil Engineering  

Uncover the topics revealed within the White Paper: 

• How LiDAR benefits infrastructure projects across various stages.  

• The science behind LiDAR.  

• The evolution of LiDAR technology in a clear visualization of significant milestones.   

• A breakdown of the current LiDAR technology market state and future projections.  

• How the main technical components of LiDAR systems in civil engineering interact.   

• Airborne, terrestrial, mobile, UAV (drone), and solid-state types of LiDAR.  

• Noteworthy use cases within the civil engineering domain.  

• Typical kinds of LiDAR data processing explained.  

• Common industry standards that are already in use and those under development.  

• An ultimate compilation of trusted professional associations.  

• Leading figures, key contributors, and regulatory bodies associated with LiDAR technology in civil engineering.   

• List of industry-recognized certifications for individuals, organizations, and specialized training programs.    

• A checklist of questions to ask while assessing the current state of LiDAR technology integration within your organization + a bonus list of available tools to ease your evaluation process.  

• Books, websites, articles, publications, online courses, and tutorials to deepen your knowledge, and thematic conferences for networking opportunities.  

Get access to LiDAR knowledge that drives informed decision-making, optimizes resource allocation, and enhances your project outcomes.  

Download your copy for free today. Stay tuned for more updates. 

Featured Case Studies: 

• Intetics Developed a Comprehensive, Cost-Effective Solution for Deformation and Slope Monitoring and Analysis 
• A Remote In-Sourcing® Team Helped a Powerlines Management Company Cut Data Processing Time by 65% 
• The Platform for Extracting Features from LiDAR Data Decreased the Manual Processing Time by 60% 
• Remote In-Sourcing® Team of 150 FTE Enabled Mobile Mapping Company to Process 7,000 km of Road Data and Reduce QA Costs by 63% 

The post LiDAR Technology for Civil Engineering: The A to Z of Light Detection and Ranging appeared first on Intetics.

But! Copilot may not be a one-size-fits-all solution. Different developers have different preferences, workflows, and needs. Recognizing this diversity, let’s explore alternative tools that offer similar code completion features (and more). 

Quick Intro to Useful Alternative Plugins to GitHub Copilot 

We’ll delve into three notable alternatives of GitHub Copilot: Tabnine, AWS Codewhisperer, and Bito.  

Before diving into the distinctive features of each tool, here’s a quick look at what each one brings to the table: 

Tabnine 

Tabnine was an early player in terms of code completion tools, and it provides a practical plugin compatible with your preferred code editor. It offers suggestions for whole lines of code and full-function completions across various languages, libraries, and frameworks. And it does so based on natural-language comments. 

One noteworthy aspect of Tabnine is its efficiency with minimal context compared to Copilot. It suggests code midline as you type, without waiting for one line to be finished before moving to the next.  

For teams and organizations, Tabnine offers the capability to host and train their own AI models. This feature facilitates collaborative autocompletion across IDEs, which enhances teamwork and contributes to code security. 

Beyond code completion, Tabnine tackles unit test support. Using advanced AI, it automatically generates unit tests for your code, a helpful feature for ensuring thorough testing and code stability, especially on larger projects. 

The interface is user-friendly, requiring minimal configuration. As you use it more, Tabnine adapts and tailors unit test suggestions to your coding style.  

AWS CodeWhisperer 

Amazon CodeWhisperer understands natural language comments in English and offers suggestions ranging from snippets to entire functions—up to 10–15 lines—in various IDEs. Similar to Tabnine, CodeWhisperer adapts to your coding style and ensures a personalized coding experience. It also extends its capabilities to the command line with inline documentation and AI-driven natural-language-to-code translation.  

For those leveraging AWS services, CodeWhisperer analyzes code in real time, with suggestions tailored to AWS APIs like Amazon EC2 and AWS Lambda and aligned with best practices for cloud-based projects. 

To ensure responsible coding, the tool introduces a reference tracker for open-source code. This feature flags suggestions resembling public code with detailed annotations for review. The goal here is to actively avoid biases in code suggestions. 

If you’re choosing between AWS CodeWhisperer and Tabnine, consider what you prioritize. Let’s say you value versatility and broad language support across various editors; then Tabnine is your pick. CodeWhisperer, on the other hand, is for those who focus on precision — the tool learns from in-house code and tailors suggestions to an organization’s practices. 

Bito 

Bito allows users to generate code in any language, create comprehensive test cases, and gain insights into selected code snippets with the Explain Code feature. For documentation, Bito offers a Comment Method tool, while the Improve Performance feature provides tips for enhancing code efficiency. Security concerns are addressed through the Check Security function, which lets you inquire about potential issues.  

What sets Bito apart from GitHub Copilot is its integration with Visual Studio Code — it leverages embeddings and a vector database to understand your local codebase. This ensures context-aware suggestions, a distinctive advantage over Copilot. 

Another noteworthy feature is the Generate Unit Test Shortcut:  

The shortcut allows developers to efficiently generate test cases for different code paths and scenarios. This ensures comprehensive testing coverage and reduces the risk of undetected bugs.  

The consistent testing standards promoted by Bito foster collaboration and codebase maintainability within development teams, as all members can adhere to the same testing practices.  

On Ethics for AI Code Completion Plugins in Software Development 

Respecting intellectual property rights and privacy is crucial for ethical software development, fostering trust in the industry.

Developers using code completion tools like Tabnine, Bito, etc. should: 

• ensure that sensitive or confidential data is not intentionally or unintentionally exposed, 
• be mindful not to breach non-disclosure agreements (NDAs), 
• seek explicit consent from clients or stakeholders before employing coding assistant tools in projects involving their codebases. 

On the level of software development organizations, it’s a best practice to regulate AI tools usage in processes. Many responsible engineering companies already create and implement AI tools usage policies for employees on how to be safe and secure when using generative AI, especially when it involves the sharing of potentially sensitive company and customer information. 

Summing Up

The implementation of AI assistants like Tabnine, AWS CodeWhisperer, and Bito has resulted in tangible time savings and enhanced productivity. And we have the numbers to back this up. 

In just two weeks, Tabnine contributed to a nearly 13-hour timesaving for an Intetics engineer. That month, 27% of that engineer’s code was produced by the tool.  

With AWS CodeWhisperer, the official figures claim a 57% acceleration in developer productivity and a 27% increase in the likelihood of project success.  

As for Bito, the subjective experience reveals improvements in speed, particularly in implementing new features. The initial estimate suggested a potential 27% code contribution, but real-world results prove a more conservative productivity boost of 10-20%. Also, Bito offers remarkable efficiency, potentially saving up to 80% of time spent on writing unit tests.  

To wrap it up, embrace the tools that empower you to code better and faster. 

Featured materials: 

• ChatGPT for Software Developers: Better Code, Increased Productivity, and Premier Product Quality [With Examples of Prompts]
• Why GitHub Copilot Is the Ultimate Game-Changer for Software Developers
• AI-Augmented Project Management: 15 Ways to Automate Time-Consuming Tasks with ChatGPT [+12 Other AI Assistants]

The post Alternatives to GitHub Copilot: Tabnine, AWS CodeWhisperer, and Bito to Increase Developers’ Productivity by 20% appeared first on Intetics.

Let’s break it down and see why it’s a handy tool for programmers to speed up the coding process, learn new frameworks or libraries, or explore different ways of solving problems.  

What Is GitHub Copilot? 

GitHub Copilot is an AI pair programmer providing auto-complete style recommendations as you code. It accelerates your coding process by analyzing contextual elements like variable names, surrounding code, and function signatures, generating relevant suggestions in real time.  

The concept of an “AI pair programmer” underscores its collaborative nature, emphasizing that it complements and augments your coding efforts—it cannot work without you. 

GitHub Copilot is powered by a generative AI model developed by GitHub, OpenAI, and Microsoft. 

Once installed, the coding assistant analyzes the developer’s code, providing suggestions for completing code snippets, suggesting relevant functions, and even refactoring existing code. 

Read also: ChatGPT for Software Developers: Better Code, Increased Productivity, and Premier Product Quality [With Examples of Prompts]

Installing GitHub Copilot: Step-by-Step Guide 

GitHub Copilot integrates with various popular integrated development environments (IDEs), such as Visual Studio Code, Visual Studio, Neovim, and JetBrains IDEs.  

GitHub Copilot can be accessed through personal accounts with GitHub Copilot Individual or organizational accounts with GitHub Copilot Business. GitHub Copilot is free for verified students, teachers, and maintainers of popular open-source projects. 

You can try GitHub Copilot for free with a one-time 30-day trial. After the free trial, you will need a paid subscription for continued use. Billing plans are available at GitHub Docs. 

To get started, you’ll need the following: 

• An active GitHub Copilot subscription; 
• Microsoft Visual Studio Code (or another preferred IDE); 
• The GitHub Copilot extension installed in the Visual Studio Code. 

How to Install GitHub Copilot? 

• Find the GitHub Copilot extension page in the Visual Studio Code Marketplace and click Install. 
• Click Open Visual Studio Code in the pop-up window that appeared.  
• Click Install in the “Extension: GitHub Copilot” tab in Visual Studio Code.  
  • If you have not previously authorized Visual Studio Code in your GitHub account, you will be prompted to sign into GitHub in Visual Studio Code.  
  • If you have previously authorized Visual Studio Code for your account on GitHub, GitHub Copilot will be automatically authorized.  
• If you don’t get the prompt to authorize, click the bell icon in the bottom panel of the Visual Studio Code window. 

• In your browser, GitHub will request the necessary permissions for GitHub Copilot. To approve these permissions, click Authorize Visual Studio Code.  
• To confirm the authentication, in Visual Studio Code, in the “Visual Studio Code” dialog box, click Open. 

GitHub Copilot can be customized to align with your coding style. Configure settings like indentation, line length limits, and suggestion verbosity to make Copilot an extension of your coding preferences. 

Creation of UI Elements with Prompts in GitHub Copilot 

GitHub Copilot’s capability to generate code suggestions extends to creating user interface (UI) elements. By providing clear and concise prompts, developers can elicit relevant code snippets for various UI components, such as buttons, dropdown menus, and text fields.  

Example: We need to create a tracker of monthly income and expenses.

Let’s generate a prompt for GitHub Copilot: 

<!–bootstrap grid with 12 rows and 1 column for small screens, and 6 rows and 2 columns for medium screens and above, each cell contains label of the month from January to December, and 2 bootstrap number inputs side by side with labels, 1 for income and 1 for expenses –>) 

Based on the input, GitHub Copilot starts to suggest the code to generate the desired output. 

To accept the suggested code, just press Tab, and the assistant will write the code. 

It’s crucial to understand that Copilot isn’t flawless enough to write code independently, as we highlighted earlier, its “pair programmer” nature. You are responsible for crafting the overall logic, guiding the tool with well-thought-out prompts, and overseeing the suggestions it generates. You steer the process through practical and precise instructions while the tool accelerates the code-writing process for you.  

So, after a brief review, the developer reviews and improves the proposed structure and creates the desired table faster. 

GitHub Copilot proves to be a valuable tool for frontend developers to elicit relevant code snippets for various UI components efficiently. The example illustrates the importance of reviewing Copilot’s suggestions by an experienced developer to ensure correctness and code optimization.   

While Copilot accelerates the code-writing process, user intervention remains essential for ensuring logic, accuracy, and adherence to specific requirements. Let’s focus on how to engineer prompts that guarantee Copilot’s efficient assistance. 

Getting Desired Outcomes with Prompts: Best Practices 

Once we have reviewed the capabilities to generate entire functions, classes, or blocks of code faster, let’s touch on the best practices for prompt engineering to give clear instructions to GitHub Copilot and generate desired results. 

• High-level context in comments: Provide high-level context in a comment at the top of the file and leave more detailed instructions in the form of comments and code. 
• Provide specific details: For precise code suggestions from GitHub Copilot, provide specific details. If you aim, for example, to fetch data from an API, specify the type of data, processing method, and the target API endpoint. 
• Provide examples: Apart from instructing GitHub Copilot verbally, you can also demonstrate desired actions using examples in your preferred coding style.  
• Iterate and refine: Continuously refine your code with Copilot’s suggestions. Start with its recommendations and customize them to meet your specific requirements. Regularly review and adjust the generated code to match your coding style and project needs. 
• Validate generated code: Validate Copilot-generated code for correctness, efficiency, and adherence to standards. Conduct thorough code reviews, run comprehensive tests, and make necessary adjustments to maintain codebase quality. 

In turn, GitHub is experimenting with LLMs to evolve Copilot, focusing on creating a developer-friendly AI experience that is predictable, tolerable, steerable, and verifiable to enhance overall productivity and effectiveness. All this is done to extend GitHub Copilot across the developer lifecycle. 

Other Ways to Enhance Your Coding Workflow with GitHub Copilot 

Explore additional capabilities of GitHub Copilot to refine your coding process:  

• Code refactoring: GitHub Copilot aids in refactoring by suggesting concise and efficient alternatives, identifying redundancies, simplifying complexity, and recommending improved coding patterns for cleaner, more maintainable code.  
• Error & exception handling: Copilot assists in error handling by suggesting mechanisms and blocks for exceptions, enhancing code robustness and resilience to unexpected scenarios.  
• Customization: Enhance GitHub Copilot by customizing its training with your codebases, allowing it to generate more accurate, context-aware suggestions tailored to your specific application domain.  
• Consistency across teams: GitHub Copilot is valuable for team development, aiding in maintaining coding standards by suggesting consistent styles and practices. Its suggestions are discussion starters in code reviews, fostering collaboration, and knowledge sharing among team members. 

Summing Up 

It still requires profound expertise to build software. Even with Copilot’s help, a developer should verify and understand generated code. Non-programmers can’t jump on Copilot and make whatever they want. Or is it so yet?   

Keep a keen eye on the dynamic AI assistant’s landscape—experiment with emerging options in your environment to identify the ones that align best with your requirements. Explore tools like GitHub Copilot, and stay tuned to learn more about alternatives: Bito, Amazon CodeWhisperer, Tabnine, etc.  

Join us to empower developers to be more productive at every stage of the software development lifecycle. 

The post Why GitHub Copilot Is the Ultimate Game-Changer for Software Developers appeared first on Intetics.

But in reality, all these factors stem from one main reason – insufficient use of innovative technologies. According to McKinsey, <1% of revenue is used for R&D spending in construction companies; the same metric in the auto industry can be as high as 4.5%.

Construction companies, while generally having some of the most conservative attitudes towards technology, are nevertheless actively introducing drones into their operations. According to USG and the U.S. Chamber of Commerce, 63% of contractors are already using drones in construction projects.

And it’s not just a matter of mandatory monitoring of construction sites for workers’ safety. At a construction site, drones are useful at all stages – from preliminary assessment of site suitability to the maintenance of finished objects.

What Drones Do Better Than Humans on a Construction SiteQuickly conduct a topographic survey of the site

1.Quickly conduct a topographic survey of the site

Aerial photography helps create the most accurate 2D and 3D maps with geolocation and geodetic information (distance, elevation, elevation difference, etc.). Those can later be used to accurately estimate excavation volume or the quantity of soil for filling, choose the best placement for objects, and store building materials.

The collected data can also be used to create architectural and design projects, presentations for investors. 3D models, compiled according to all terrain attributes, can showcase everything: how buildings will cast shadows, where to plan parking or landscaping, and optimal placement of solar panels and collectors.

All you need to do is a drone and an operator. In good weather conditions, the device can work all day, returning to the base for a battery change every 40-60 minutes. One drone replaces an entire team of surveyors and a helicopter combined while also being much cheaper.

2. Accurately record the project progress

A drone is suitable for assessing how the factual results correspond to the plan based on a repeated series of surveys. This allows for more efficient project planning, up-to-date reports, and quicker dispute resolutions with contractors.

At construction sites, the actual rate of construction often differs from the plan and may not match what the field crew reported. And since all processes are connected to each other, if one thing fails, for example, monolithic construction, it might cause delays in communication or disruptions in building materials.

Drones also help estimate the amount of work for piecework payments. How can you calculate exactly how much soil was dug up and removed from the site? By weighing every truck? There is a better option: you can place points with geospatial information on the detailed pictures of the construction pits. Those can be used as a basis to accurately calculate the volume of soil taken from pits and trenches. A similar approach is suitable for calculating dimensions of foundations, walls, floors, rafter systems, etc.

3. Share up-to-date data promptly

One person would need several days to shoot a large construction site from different angles. In the meantime, some things might change and require additional shots. If necessary, drones make it easier to update the site map on a daily basis and even monitor the processes in real-time.

Where Can You Save Money by Using a Drone on a Construction Site?

• Design. 3D maps of the area allow you to create projects of future buildings faster and more accurately than when you work with on-field surveyors.
• Settlements with contractors. They will be as accurate and fair as possible since they’re based on what was actually carried out. A drone helps you record volumes, timing, and the rate of construction.
• Geodetic surveys. A drone can accomplish a task that would take people 2-3 weeks in 2-3 days.
• Creating presentations and reports for investors and clients.
• Walking tours on the field. You can spend less time on planned routine examinations and on eliminating impact from emergencies (because you can spot them immediately or prevent them).
• Manual examinations. A drone does not need any belay devices or personal protective equipment. It has no problem shooting angles that a person with a total station and a tape measure couldn’t ever do.
• Document digitization. A bit of conservatism is helpful when it comes to safety and accuracy. But outdated formats like paper drawings, projects, estimates, logs, and reports just slow down the construction process. Drones help you start to digitize terrain maps, visual reports, and image-based drawings. This helps you analyze and share data faster.
• Maintenance of finished objects. Drones are useful for inspecting roofs, engineering units, ceilings, structural supports, etc.
• Subsequent projects. No objects are the same, but there are typical and similar ones. After estimating the amount of work for one object and comparing the estimates with actual results, you can rely on this data for other projects.

Lidar, GPR, PTZ cameras – these are your new employees. They will never get tired or go on vacation during a peak season.

We should also point out that drones with additional attachments can help directly in the work process: pull electrical cables at a height and deliver tools. At some point in the future, human interference will only be required at the very end of construction – to get access keys to the robot cleaner. It won’t be soon, so, for now, let’s start with just drones!

Find more about Drones :

Download The New White Paper:

1. The Ultimate UAV (Drones) Technologies Overview by Tech Geeks

Solutions:

1. Field Fata Collection Solution
2. Points of Interest Data for Business
3. Drone Data Geoportal

The post How to Build More Cost-Efficiently and Safely with Drones appeared first on Intetics.