As a developing country, Fiji is facing rapid urbanisation, which is visible in the massive development projects that include housing, roads, and civil works. In this study, we present machine learning and remote sensing frameworks to compare land use and land cover change from 2013 to 2024 in Nadi, Fiji. The ultimate goal of this study is to provide technical support in land cover/land use modelling and change detection. We used Landsat-8 satellite image for the study region and created our training dataset with labels for supervised machine learning. We used Google Earth Engine and unsupervised machine learning via k-means clustering to generate the land cover map. We used convolutional neural networks to classify the selected regions' land cover types. We present a visualisation of change detection, highlighting urban area changes over time to monitor changes in the map.

DeepImageJ has been updated to DeepImageJ 2.1. The format of the models in previous versions are not compatible with DeepImageJ 2.1. Please, try to update your models using DeepImageJ Build Bundled Model or do not update DeepImageJ in Fiji using the Update Sites until you can update your models. Contact us if you have any question! DeepImageJ is a user-friendly plugin that enables the use of a variety of pre-trained deep learning models in ImageJ and Fiji.

Unlike the Cylons in Battlestar Galactica, however, this infiltrator was on a peaceful mission. In a new study published in Science Robotics, researchers at MIT unveil what they say is the most advanced robotic fish of its kind ever built. Armed with a camera and a lifelike wiggle, the device could one day help biologists monitor the health of marine habitats without stressing out their aquatic denizens. The Soft Robotic Fish, SoFi for short, is 18.5 inches long from snout to tail and weighs about 3.5 pounds. It can dive 60 feet underwater and is powered by enough juice for about 40 minutes of exploration. As climate change and overfishing wreak havoc on oceans, scientists are racing to study marine life in detail.

Fish, like most animals, have a pretty good idea of which other animals they're cool with, and which animals they're not. Very few animals are cool with humans, and fish are no exception--maybe they're afraid, maybe they're curious, and maybe they'll pretend to ignore you until you get too close, but in any of these cases, your presence is affecting their behavior. We've seen many clever examples of animal behavior researchers using robots to study their subjects up close with minimal disruption, and in a paper published in Science Robotics today, roboticists at MIT's Computer Science and Artificial Intelligence Laboratory describe a new kind of soft robotic spy fish that can more or less blend right in with everything else living on a coral reef. SoFi, MIT's soft robotic fish, is designed to provide close-range, minimally disruptive observations of all the fascinating and adorable animals that live underwater. The MIT roboticists (Robert K. Katzschmann, Joseph DelPreto, Robert MacCurdy, and Professor Daniela Rus) were careful to make SoFi as similar in size and behavior to a real fish as was possible, but they also had to make it completely self-contained and actually useful--SoFi isn't just a proof-of-concept for the design of a biomimetic robotic fish, it's a real research tool, with a friendly control system, and practical battery life.

It looks and moves like a real fish, flapping its tail from side to side. But this fish is controlled by a human diver via a waterproofed Super Nintendo controller and an ultrasound transmitter. SoFi, the soft robotic fish, has been designed to let researchers study marine life up close. Remotely-operated or autonomous submersibles are usually propeller-driven, which tends to disturb wildlife. Videos of test dives in Fiji's Rainbow Reef show SoFi skirting over coral alongside real fish, which seem unfazed by the mechanical interloper.

The microscope is mainly used for imaging applications to analyze terabytes of data per day. These applications can profit by late advances in computer vision and profound learning. Now, in collaboration with robotic microscopy applications, Google engineers have assembled high-quality image datasets that separate signal from noise. In "Assessing Microscope Image Focus Quality with Deep Learning", researchers trained a deep neural network to rate the focus quality of microscopy images with higher accuracy than previous methods. They added the pre-trained TensorFlow model with plugins in Fiji (ImageJ) and CellProfiler, two leading open source scientific image analysis tools to use with the graphical user interface or invoked via scripts.