New AI features in Google Earth let users ask chatbot-style questions to find changes in the climate. The system could eventually predict disasters and identify the communities likely to be affected. Google has come up with a way to better map Earth's disasters, predict them, and be able to track which communities and ecosystems are going to be wrought by their destruction. If you want to find out what's straining the environment in your neck of the woods, all you have to do is ask. Google Earth AI, a fusion of Google's Earth and Gemini AI systems, was introduced in July .

Google Earth is celebrating its 20th anniversary by allowing users to access historical Street View images directly in the geographic visualization app. Users can scroll through older views of locations and see how areas have changed over time. The historical Street View images are already available in Google Maps. Last year, Google also added the ability to view historical satellite and aerial photos directly in Google Earth--material that was previously only available in the Earth Pro desktop app. In the coming weeks, Google also plans to launch a new feature that will provide professional users with AI-based insights about Earth.

This paper describes the mwBTFreddy dataset, a resource developed to support flash flood damage assessment in urban Malawi, specifically focusing on the impacts of Cyclone Freddy in 2023. The dataset comprises paired pre- and post-disaster satellite images sourced from Google Earth Pro, accompanied by JSON files containing labelled building annotations with geographic coordinates and damage levels (no damage, minor, major, or destroyed). Developed by the Kuyesera AI Lab at the Malawi University of Business and Applied Sciences, this dataset is intended to facilitate the development of machine learning models tailored to building detection and damage classification in African urban contexts. It also supports flood damage visualisation and spatial analysis to inform decisions on relocation, infrastructure planning, and emergency response in climate-vulnerable regions.

Due to climate change, the extreme wildfire has become one of the most dangerous natural hazards to human civilization. Even though, some wildfires may be initially caused by human activity, but the spread of wildfires is mainly determined by environmental factors, for examples, (1) weather conditions such as temperature, wind direction and intensity, and moisture levels; (2) the amount and types of dry vegetation in a local area, and (3) topographic or local terrian conditions, which affects how much rain an area gets and how fire dynamics will be constrained or faciliated. Thus, to accurately forecast wildfire occurrence has become one of most urgent and taunting environmental challenges in global scale. In this work, we developed a real-time Multimodal Transformer Neural Network Machine Learning model that combines several advanced artificial intelligence techniques and statistical methods to practically forecast the occurrence of wildfire at the precise location in real time, which not only utilizes large scale data information such as hourly weather forecasting data, but also takes into account small scale topographical data such as local terrain condition and local vegetation conditions collecting from Google Earth images to determine the probabilities of wildfire occurrence location at small scale as well as their timing synchronized with weather forecast information. By using the wildfire data in the United States from 1992 to 2015 to train the multimodal transformer neural network, it can predict the probabilities of wildfire occurrence according to the real-time weather forecast and the synchronized Google Earth image data to provide the wildfire occurrence probability in any small location ($100m^2$) within 24 hours ahead.

Unmanned Aerial Vehicle (UAV) based remote sensing system incorporated with computer vision has demonstrated potential for assisting building construction and in disaster management like damage assessment during earthquakes. The vulnerability of a building to earthquake can be assessed through inspection that takes into account the expected damage progression of the associated component and the component's contribution to structural system performance. Most of these inspections are done manually, leading to high utilization of manpower, time, and cost. This paper proposes a methodology to automate these inspections through UAV-based image data collection and a software library for post-processing that helps in estimating the seismic structural parameters. The key parameters considered here are the distances between adjacent buildings, building plan-shape, building plan area, objects on the rooftop and rooftop layout. The accuracy of the proposed methodology in estimating the above-mentioned parameters is verified through field measurements taken using a distance measuring sensor and also from the data obtained through Google Earth. Additional details and code can be accessed from https://uvrsabi.github.io/ .

Can't figure out how to use your new tech toy? While you may have found a new phone, smart speaker, tablet or laptop under the tree this holiday season, you might be a little overwhelmed with all its features. In fact, whether you're tech-savvy or tech-shy, many of us stick to what we know and repeat those actions over and over, opposed to venturing a little outside our comfort zone. That's ok, of course, but should you want to learn a few tech tips and tricks – to help save you time, money and stress – we've got a half-dozen of ideas here for you, covering a wide range of popular products. Typing on your iPhone and want to undo what you just wrote?

Panaji: A team from Don Bosco College of Engineering, Fatorda, in a project sponsored by ISRO, has developed an algorithm that enables accurate identification of land features like forests, waterbodies, etc, using satellite images. Unlike applications like Google Earth, the machine-learning algorithm even helps identify details like the type of crops being cultivated in a field. The tool is expected to be immensely helpful in town and country planning, and in carrying out environmental studies, among other uses. Rahul Kotru, Musab Shaikh and Satyaswarup Banerjee of the electronics and telecommunication (ETC) branch have developed the deep learning algorithm, under the guidance of lead scientist, Varsha Turkar, who heads the department, and Shreyas Simu. This data can be captured during day and night independent of weather and climatic conditions.

The world is indeed lucky when our most brilliant minds choose to work for the common good, rather than chasing money or becoming master criminals. So Inhabitat wants to thank young Ryan Honary for his work on an early detection system for wildfires. Sickened by the losses people sustained in the 2018 Camp Fire, California's deadliest wildfire, Honary turned his attention to how to mitigate future disasters. In 2019, Honary won the $10,000 grand prize in the Ignite Innovation Student Challenge for his Early Wildfire Detection Network submission, which provides app technology to firefighters. He was only in fifth grade at the time.

TechRepublic's Karen Roby spoke with Joe Wolfel, co-CEO of Terradepth, about the company's ocean data-collection robot. The following is an edited transcript of their conversation. Karen Roby: I think this is a good way to summarize that what you guys are doing and are working toward is a fleet of fully autonomous deep ocean data collection submarines. Tell us a little bit about how this came about? I mean, you don't just wake up one day and say, yeah this is what I think I'm going to do.