Joan Li, Author at GeoNadir

Multispectral mapping drone flying over grassy background

Drone Data Processing

Multispectral mapping Made Easy? Hands-On with the DJI Mavic 3 Multispectral and Terra App

Integrating drone technology with cutting-edge software has revolutionized the way industries collect, process, and analyze geospatial data. One of the most versatile tools in this field is the DJI Mavic 3 Multispectral (Mavic 3M), a drone designed for precision agriculture, environmental monitoring, and land management. DJI also provides its own data processing software, DJI Terra,

Drone Mapping Tips & Tricks

How many GCPs do I need for drone mapping?

When it comes to geospatial data analysis and mapping, accuracy is crucial. One important tool that helps achieve high accuracy is ground control points (GCPs). In this blog post, I want to share my experience processing one of our recent datasets using different numbers of GCPs ranging from 1 to 5. Join me as we

Drone Mapping Tips & Tricks

Using Dronelink for drone mapping: a review

When it comes to using drones for mapping, having a good mission planning app can make all the difference in the world. In the past, we’ve used apps like DroneDeploy, Pix4d Capture, Map Pilot and the DJIGo4. But with the release of the DJI Mini and Air series, as well as the fact that the

Artificial Intelligence

How Artificial Intelligence helps make drone image processing smarter

By combining AI with drone image processing, we can pinpoint the exact geographical location on earth in an efficient and consistent manner. If you search “AI”, there are more than four trillion results in Google. If you search for “AI” in news, there is a piece released almost every hour about this topic. Artificial

Artificial Intelligence

Machine Learning Drones: 3 valuable concepts you need to know

We’ve been talking about artificial intelligence (AI), drones and machine learning for a while now. However, do you feel that sometimes it’s just a bunch of fancy words flying around? In this article, I would like to introduce three basic machine learning concepts for those who love drones. No matter why you love flying drones, or

Cow Photo by Wolfgang Hasselmann on Unsplash

Artificial Intelligence

Moo alert! We want to create an outstanding pasture for out standing cows!

Cattle are definitely one of the rock stars in human history. They have been contributing to our society as draught animals, providing resources such as meat, milk, leather, fertiliser, by-products and as trusting companions and possessions. Why did the cow get an award? For being out-standing in the field. (Photo by Wolfgang Hasselmann on Unsplash)

Drone Data Capture

What can we do with drone data? A few advantages of drones

Get more information about the advantages of drones. Drone images hold a wealth of information. From an individual pixel to a combination of images creating orthomosaics over space and time. In the opening ceremony of the 2020 Tokyo Olympics, the drone globe on top of the stadium is for sure one of the most iconic

Artificial Intelligence

How much data does a drone image have?

In this article, we discuss extracting information from drone images. From orthomosaic maps to pixels, thrilling information can be unveiled layer by layer from drone data. However, some may wonder, how is this related to us? These are scientists’ jobs. On one hand, the abundant amount of raw data out there is too cumbersome for

Artificial Intelligence

7 Terms I Found Really Confusing in Computer Vision for Beginners (Tutorial)

If the relationships among artificial intelligence, machine learning, and deep learning were one thing that confuses me the most at the beginning of my data science journey, countless terms in computer vision are not helping with the rest of the journey. However, there is no way for me to avoid them since drone data are

Artificial Intelligence

GPU vs CPU: Which One Do You Need If You Want to Learn Deep Learning

Let us explain the difference between CPU vs GPU in the process of deep learning. Recently, I had an interesting experience while training a deep learning model. To make a long story short, I’ll tell you the result first: CPU based computing took 42 minutes to train over 2000 images for one epoch, while GPU based computing only took

Author: Joan Li