Deep learning models have shown encouraging capabilities for mapping accurately forests at medium resolution with TanDEM-X interferometric SAR data. Such models, as most of current state-of-the-art deep learning techniques in remote sensing, are trained in a fully-supervised way, which requires a large amount of labeled data for training and validation. In this work, our aim is to exploit the high-resolution capabilities of the TanDEM-X mission to map forests at 6 m. The goal is to overcome the intrinsic limitations posed by midresolution products, which affect, e.g., the detection of narrow roads within vegetated areas and the precise delineation of forested regions contours. To cope with the lack of extended reliable reference datasets at such a high resolution, we investigate self-supervised learning techniques for extracting highly informative representations from the input features, followed by a supervised training step with a significantly smaller number of reliable labels. A 1 m resolution forest/non-forest reference map over Pennsylvania, USA, allows for comparing different training approaches for the development of an effective forest mapping framework with limited labeled samples. We select the best-performing approach over this test region and apply it in a real-case forest mapping scenario over the Amazon rainforest, where only very few labeled data at high resolution are available. In this challenging scenario, the proposed self-supervised framework significantly enhances the classification accuracy with respect to fully-supervised methods, trained using the same amount of labeled data, representing an extremely promising starting point for large-scale, very high-resolution forest mapping with TanDEM-X data.

After the introduction of Large Language Models (LLMs), there have been substantial improvements in the performance of Natural Language Generation (NLG) tasks, including Text Summarization and Machine Translation. However, LLMs still produce outputs containing hallucinations, that is, content not grounded in factual information. Therefore, developing methods to assess the factuality of LLMs has become urgent. Indeed, resources for factuality evaluation have recently emerged. Although challenging, these resources face one or more of the following limitations: (i) they are tailored to a specific task or domain; (ii) they are limited in size, thereby preventing the training of new factuality evaluators; (iii) they are designed for simpler verification tasks, such as claim verification. To address these issues, we introduce LLM-Oasis, to the best of our knowledge the largest resource for training end-to-end factuality evaluators. LLM-Oasis is constructed by extracting claims from Wikipedia, falsifying a subset of these claims, and generating pairs of factual and unfactual texts. We then rely on human annotators to both validate the quality of our dataset and to create a gold standard test set for benchmarking factuality evaluation systems. Our experiments demonstrate that LLM-Oasis presents a significant challenge for state-of-the-art LLMs, with GPT-4o achieving up to 60% accuracy in our proposed end-to-end factuality evaluation task, highlighting its potential to drive future research in the field.

Large language models (LLMs) demonstrate remarkable performance across various tasks, prompting researchers to develop diverse evaluation benchmarks. However, existing benchmarks typically measure the ability of LLMs to respond to individual questions, neglecting the complex interactions in real-world applications. In this paper, we introduce Compound Question Synthesis (CQ-Syn) to create the Compound-QA benchmark, focusing on compound questions with multiple sub-questions. This benchmark is derived from existing QA datasets, annotated with proprietary LLMs and verified by humans for accuracy. It encompasses five categories: Factual-Statement, Cause-and-Effect, Hypothetical-Analysis, Comparison-and-Selection, and Evaluation-and-Suggestion. It evaluates the LLM capability in terms of three dimensions including understanding, reasoning, and knowledge. Our assessment of eight open-source LLMs using Compound-QA reveals distinct patterns in their responses to compound questions, which are significantly poorer than those to non-compound questions. Additionally, we investigate various methods to enhance LLMs performance on compound questions. The results indicate that these approaches significantly improve the models' comprehension and reasoning abilities on compound questions.

A millennium of endeavors to fully recognize and foresee the evolution of dynamic environments has produced many mathematical models for forecasting, and information-gathering techniques, but also exceptionally complicated computational systems. Predefined complicated realities called hyperchaotic frameworks [1] demonstrate unpredictable sequences of behavior over time and sometimes defy standards. These events' temporal and spatial relationships can be compared to physiological kinetics [2]. Several complicated frameworks are currently developed to comprehend spontaneous incidents, their erratic conduct, and how changing the circumstances of actual events may result in an unanticipated shift in the result. Over the duration of the past couple of eons, the objective of being able to understand and anticipate unpredictable actions has been accomplished with the aid of innovations in technology [3] and fundamental principles [4].

The Multimodal Learning for Earth and Environment Workshop (MultiEarth 2023) is the second annual CVPR workshop aimed at the monitoring and analysis of the health of Earth ecosystems by leveraging the vast amount of remote sensing data that is continuously being collected. The primary objective of this workshop is to bring together the Earth and environmental science communities as well as the multimodal representation learning communities to explore new ways of harnessing technological advancements in support of environmental monitoring. The MultiEarth Workshop also seeks to provide a common benchmark for processing multimodal remote sensing information by organizing public challenges focused on monitoring the Amazon rainforest. These challenges include estimating deforestation, detecting forest fires, translating synthetic aperture radar (SAR) images to the visible domain, and projecting environmental trends. This paper presents the challenge guidelines, datasets, and evaluation metrics. Our challenge website is available at https://sites.google.com/view/rainforest-challenge/multiearth-2023.

It took just the month of March this year to fell an area of forest in Triunfo do Xingu equivalent to 700 football pitches. At more than 16,000 sq km, this Environmental Protection Area (APA) in the south-eastern corner of the Brazilian Amazon, in the state of Pará, is one of the largest conservation areas in the world. And according to a new tool that predicts where deforestation will happen next, it's also the APA at highest risk of even more destruction. The tool, PrevisIA, is an artificial intelligence platform created by researchers at environmental nonprofit Imazon. Instead of trying to repair damage done by deforestation after the fact, they wanted to find a way to prevent it from happening at all.

This is Chapter 8 on the book Grokking Machine Learning. Check out the author's YouTube channel Serrano.Academy for lots of machine learning videos! Take 40% off Grokking Machine Learning by entering fccserrano into the discount code box at checkout at manning.com. Naive Bayes is an important machine learning model used for prediction. The naive Bayes model is a purely probabilistic classification model, which means the prediction is a number between 0 and 1, indicating the probability that a label is positive.

Warmer temperatures may benefit parts of the Amazon rainforest, suggesting that the tropical ecosystem may be more resistant to climate change than once thought. It had previously been thought that water stress brought on by global warming and the drying out of the soil and air would broadly harm the plants of the Amazon. This would lead to reduced photosynthesis -- the chemical process by which plants make food and absorb in carbon dioxide -- and help accelerate climate change. However, US researchers found that wetter areas of the world's largest rainforest actually grow leaves more efficient at photosynthesis when exposed to dry air. The team warned that there is a limit to this, however, and that excessively warm temperatures would still cause damage to even these resilient parts of the forest.

Forests are the major terrestrial ecosystem responsible for carbon sequestration and storage. The Amazon rainforest is the world's largest tropical rainforest encompassing up to 2,124,000 square miles, covering a large area in South America including nine countries. The majority of that area (69%) lies in Brazil. Thus, Amazonia holds about 20% of the total carbon contained in the world's terrestrial vegetation.1,5,7 But the rampant deforestation due to illegal logging, mining, cattle ranching, and soy plantation are examples of threats to the vast region.