This blog explains one my projects while working as an intern in New Zealand at Lynker Analytics. The goal of this work was to explore Machine Learning tools available within the Esri platform to create new maps of Hamilton.

Earlier this year the Lynker Analytics team completed a bicycle 360 video survey of Te Awa Kairangi/Hutt River on the true left between Harcourt Park north of Upper Hutt to the river mouth in Petone.

This blog summarises the technical processes used in gathering, integrating and disseminating data from stream surveys. This is based on processes trialled for the Raumanga Stream Survey (Whangarei) with a view to future reuse

We have long pondered the best way to use more information than a simple 3-band RGB image to train a CNN model. But why, you ask, is this an issue? Surely if you have a four band or multi-band image this will be intuitively better than a simple three bands

This blog post covers the creation of an automated process to develop a large quantity of quality data to train a Stylegans model, which may generate realistic images of tarakihi. This is to assist in stock assessment of tarakihi

Over the past few months Lynker Analytics together with our partners Action Aviation and UAV Mapping NZ have flown the entire length of Aotearoa New Zealand capturing aerial photography for the Ministry for the Environment.

Our work with location aware machine learning covers a broad range of industries and use cases from land cover mapping and habitat monitoring e.g. beaver ponds, riparian margins, wetland extent, through to fish detection and asset inspection

Solar power in New Zealand is on the rise. According to the Electricity Authority, New Zealand had more than 34,000 grid-connected photovoltaic (PV) installations at the end of August 2021, a total combined install capacity of over 170 MW.

Shadows caused by clouds, buildings or trees obscuring the light source are commonplace in remote sensing images. These shadows are typically of a different colour than the remainder of the image and they can impair the visual quality.

This is the second instalment of our blog about the NOAA Nvidia GPU Hackathon. Part 1 of the blog described and focused on general introduction and the data used for the hackathon, this part focuses on the algorithms used and the results obtained.

Lynker Analytics in collaboration with Ai.Fish was selected for the NOAA fisheries & Nvidia GPU Hackathon. The Hackathon was a multi-day event designed to help teams of three to six developers accelerate their own code on high-end Nvidia GPUs using a programming model, or machine learning framework of their choice.

Over the course of this year we have been modelling roof top solar power generation across our towns and cities from a Harrisons Energy 3 KW solar panel system. To calculate this, we use Lidar data to model solar potential city-wide.

Yesterday NVIDIA announced its next generation of video cards, the GeForce RTX 30 series. Based upon the gaming and graphics variant of NVIDIA’s Ampere architecture and built on an optimised version of Samsung’s 8nm process.

“Serverless” has been a buzzword ever since it hit the web scene several years ago. It refers to the utilisation of managed computing services from a cloud service provider (Amazon Web Services, Microsoft Azure, etc.).

Understanding land activity at a city or region scale is vital to change detection, decision making and planning. In recent years, machine learning has evolved to the stage where more automated computer vision is possible.