Scientists have a lot of questions about our planet's most important carbon sink--and a new project could help answer them. NEW YORK, NEW YORK - APRIL 16: Eric Schmidt, former chairman and CEO at GOOGLE visits Fox Business Network Studios on April 16, 2019 in New York City. A foundation created by Eric Schmidt, the former CEO of Google, will fund a project to send drone boats out into the rough ocean around Antarctica to collect data that could help solve a crucial climate puzzle. The project is part of a suite of funding announced today from Schmidt Sciences, which Schmidt and his wife Wendy created to focus on projects tackling research into the global carbon cycle. It will spend $45 million over the next five years to fund these projects, which includes the Antarctic research.

Current optical vegetation indices (VIs) for monitoring forest ecosystems are widely used in various applications. However, continuous monitoring based on optical satellite data can be hampered by atmospheric effects such as clouds. On the contrary, synthetic aperture radar (SAR) data can offer insightful and systematic forest monitoring with complete time series due to signal penetration through clouds and day and night acquisitions. The goal of this work is to overcome the issues affecting optical data with SAR data and serve as a substitute for estimating optical VIs for forests using machine learning. Time series of four VIs (LAI, FAPAR, EVI and NDVI) were estimated using multitemporal Sentinel-1 SAR and ancillary data. This was enabled by creating a paired multi-temporal and multi-modal dataset in Google Earth Engine (GEE), including temporally and spatially aligned Sentinel-1, Sentinel-2, digital elevation model (DEM), weather and land cover datasets (MMT-GEE). The use of ancillary features generated from DEM and weather data improved the results. The open-source Automatic Machine Learning (AutoML) approach, auto-sklearn, outperformed Random Forest Regression for three out of four VIs, while a 1-hour optimization length was enough to achieve sufficient results with an R2 of 69-84% low errors (0.05-0.32 of MAE depending on VI). Great agreement was also found for selected case studies in the time series analysis and in the spatial comparison between the original and estimated SAR-based VIs. In general, compared to VIs from currently freely available optical satellite data and available global VI products, a better temporal resolution (up to 240 measurements/year) and a better spatial resolution (20 m) were achieved using estimated SAR-based VIs. A great advantage of the SAR-based VI is the ability to detect abrupt forest changes with a sub-weekly temporal accuracy.

As Artificial Intelligence (AI) continues to advance rapidly, it becomes increasingly important to consider AI's ethical and societal implications. In this paper, we present a bottom-up mapping of the current state of research at the intersection of Human-Centered AI, Ethical, and Responsible AI (HCER-AI) by thematically reviewing and analyzing 164 research papers from leading conferences in ethical, social, and human factors of AI: AIES, CHI, CSCW, and FAccT. The ongoing research in HCER-AI places emphasis on governance, fairness, and explainability. These conferences, however, concentrate on specific themes rather than encompassing all aspects. While AIES has fewer papers on HCER-AI, it emphasizes governance and rarely publishes papers about privacy, security, and human flourishing. FAccT publishes more on governance and lacks papers on privacy, security, and human flourishing. CHI and CSCW, as more established conferences, have a broader research portfolio. We find that the current emphasis on governance and fairness in AI research may not adequately address the potential unforeseen and unknown implications of AI. Therefore, we recommend that future research should expand its scope and diversify resources to prepare for these potential consequences. This could involve exploring additional areas such as privacy, security, human flourishing, and explainability.

A farmer in Niger tends to a tree sprout growing among his millet crop.Tony Rinaudo/World Vision Australia This story was originally published by Yale Environment 360 and is reproduced here as part of the Climate Desk collaboration. For decades, there have been reports of the deforestation in Africa. And they are true--the continent's forests are disappearing, lost mainly to expanding agriculture, logging, and charcoal-making. Maybe not, according to new satellite data analyzed by artificial intelligence and a growing body of on-the-ground studies. This new research is finding ever more trees outside forests, many of them nurtured by farmers and sprouting on their previously treeless fields.

People have recently begun communicating their thoughts and viewpoints through user-generated multimedia material on social networking websites. This information can be images, text, videos, or audio. Recent years have seen a rise in the frequency of occurrence of this pattern. Twitter is one of the most extensively utilized social media sites, and it is also one of the finest locations to get a sense of how people feel about events that are linked to the Monkeypox sickness. This is because tweets on Twitter are shortened and often updated, both of which contribute to the platform's character. The fundamental objective of this study is to get a deeper comprehension of the diverse range of reactions people have in response to the presence of this condition. This study focuses on finding out what individuals think about monkeypox illnesses, which presents a hybrid technique based on CNN and LSTM. We have considered all three possible polarities of a user's tweet: positive, negative, and neutral. An architecture built on CNN and LSTM is utilized to determine how accurate the prediction models are. The recommended model's accuracy was 94% on the monkeypox tweet dataset. Other performance metrics such as accuracy, recall, and F1-score were utilized to test our models and results in the most time and resource-effective manner. The findings are then compared to more traditional approaches to machine learning. The findings of this research contribute to an increased awareness of the monkeypox infection in the general population.

AI-powered camera traps are being used for more than just documenting and monitoring animals -- they have also been a crucial tool in protecting the local wildlife from poachers, such is the case in Gabon in Central Africa. Congo, and Congo Basin, in particular, offer incredible biodiversity with roughly 400 species of mammals and 1,000 species of birds that reside in the largest area of forest preserve -- 80% of Gabon is covered in forests -- out of all African nations, reports Appsilon. Out of these diverse species are endangered wildlife -- elephants, bonobos, lowland gorillas, and chimpanzees, which are at the forefront of the country's so-called "Green Gabon" movement. It seeks to develop sustainable logging while preserving wildlife, with the help of various tracking systems using satellite imagery as well as camera traps on the ground. To help maintain Gabon's biodiversity, researchers from the University of Stirling in the United Kingdom have begun using a new kind of camera trap.

There's a strand of thinking, from sci-fi films to Stephen Hawking that suggests artificial intelligence (AI) could spell doom for humans. But conservationists are increasingly turning to AI as an innovative tech solution to tackle the biodiversity crisis and mitigate climate change. From camera trap and satellite images to audio recordings, the report notes: "AI can learn how to identify which photos out of thousands contain rare species; or pinpoint an animal call out of hours of field recordings – hugely reducing the manual labour required to collect vital conservation data." AI is helping to protect species as diverse as humpback whales, koalas and snow leopards, supporting the work of scientists, researchers and rangers in vital tasks, from anti-poaching patrols to monitoring species. With machine learning (ML) computer systems that use algorithms and models to learn, understand and adapt, AI is often able to do the job of hundreds of people, getting faster, cheaper and more effective results.

Baccini et al. (Reports, 13 October 2017, p. 230) report MODIS-derived pantropical forest carbon change, with spatial patterns of carbon loss that do not correspond to higher-resolution Landsat-derived tree cover loss. The assumption that map results are unbiased and free of commission and omission errors is not supported. The application of passive moderate-resolution optical data to monitor forest carbon change overstates our current capabilities. Baccini et al. (1) report net tropical forest aboveground carbon stock change from Moderate Resolution Imaging Spectroradiometer (MODIS) data and purport to capture all forest carbon dynamics resulting from both natural and anthropogenic processes. We believe their method and results overstate current monitoring capabilities and may confuse the global community of practitioners working to establish robust and defensible forest carbon monitoring systems.