Rapid Deforestation and Burned Area Detection using Deep Multimodal Learning on Satellite Imagery

Deforestation estimation and fire detection in the Amazon forest poses a significant challenge due to the vast size of the area and the limited accessibility. However, these are crucial problems that lead to severe environmental consequences, including climate change, global warming, and biodiversity loss. To effectively address this problem, multimodal satellite imagery and remote sensing offer a promising solution for estimating deforestation and detecting wildfire in the Amazonia region. This research paper introduces a new curated dataset and a deep learning-based approach to solve these problems using convolutional neural networks (CNNs) and comprehensive data processing techniques. Our dataset includes curated images and diverse channel bands from Sentinel, Landsat, VIIRS, and MODIS satellites. We design the dataset considering different spatial and temporal resolution requirements. Our method successfully achieves high-precision deforestation estimation and burned area detection on unseen images from the region. Our code, models and dataset are open source: https://github.com/h2oai/cvpr-multiearth-deforestation-segmentationComment: CVPR 2023 Workshop on Multimodal Learning for Earth and Environment (MultiEarth

Real-Time Under-Display Cameras Image Restoration and HDR on Mobile Devices

The new trend of full-screen devices implies positioning the camera behind the screen to bring a larger display-to-body ratio, enhance eye contact, and provide a notch-free viewing experience on smartphones, TV or tablets. On the other hand, the images captured by under-display cameras (UDCs) are degraded by the screen in front of them. Deep learning methods for image restoration can significantly reduce the degradation of captured images, providing satisfying results for the human eyes. However, most proposed solutions are unreliable or efficient enough to be used in real-time on mobile devices. In this paper, we aim to solve this image restoration problem using efficient deep learning methods capable of processing FHD images in real-time on commercial smartphones while providing high-quality results. We propose a lightweight model for blind UDC Image Restoration and HDR, and we also provide a benchmark comparing the performance and runtime of different methods on smartphones. Our models are competitive on UDC benchmarks while using x4 less operations than others. To the best of our knowledge, we are the first work to approach and analyze this real-world single image restoration problem from the efficiency and production point of view.Comment: ECCV 2022 AIM Worksho

Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration

Compression plays an important role on the efficient transmission and storage of images and videos through band-limited systems such as streaming services, virtual reality or videogames. However, compression unavoidably leads to artifacts and the loss of the original information, which may severely degrade the visual quality. For these reasons, quality enhancement of compressed images has become a popular research topic. While most state-of-the-art image restoration methods are based on convolutional neural networks, other transformers-based methods such as SwinIR, show impressive performance on these tasks. In this paper, we explore the novel Swin Transformer V2, to improve SwinIR for image super-resolution, and in particular, the compressed input scenario. Using this method we can tackle the major issues in training transformer vision models, such as training instability, resolution gaps between pre-training and fine-tuning, and hunger on data. We conduct experiments on three representative tasks: JPEG compression artifacts removal, image super-resolution (classical and lightweight), and compressed image super-resolution. Experimental results demonstrate that our method, Swin2SR, can improve the training convergence and performance of SwinIR, and is a top-5 solution at the "AIM 2022 Challenge on Super-Resolution of Compressed Image and Video".Comment: European Conference on Computer Vision (ECCV 2022) Workshop

Model-Based Image Signal Processors via Learnable Dictionaries

Digital cameras transform sensor RAW readings into RGB images by means of their Image Signal Processor (ISP). Computational photography tasks such as image denoising and colour constancy are commonly performed in the RAW domain, in part due to the inherent hardware design, but also due to the appealing simplicity of noise statistics that result from the direct sensor readings. Despite this, the availability of RAW images is limited in comparison with the abundance and diversity of available RGB data. Recent approaches have attempted to bridge this gap by estimating the RGB to RAW mapping: handcrafted model-based methods that are interpretable and controllable usually require manual parameter fine-tuning, while end-to-end learnable neural networks require large amounts of training data, at times with complex training procedures, and generally lack interpretability and parametric control. Towards addressing these existing limitations, we present a novel hybrid model-based and data-driven ISP that builds on canonical ISP operations and is both learnable and interpretable. Our proposed invertible model, capable of bidirectional mapping between RAW and RGB domains, employs end-to-end learning of rich parameter representations, i.e. dictionaries, that are free from direct parametric supervision and additionally enable simple and plausible data augmentation. We evidence the value of our data generation process by extensive experiments under both RAW image reconstruction and RAW image denoising tasks, obtaining state-of-the-art performance in both. Additionally, we show that our ISP can learn meaningful mappings from few data samples, and that denoising models trained with our dictionary-based data augmentation are competitive despite having only few or zero ground-truth labels.Comment: AAAI 202

H2O Open Ecosystem for State-of-the-art Large Language Models

Large Language Models (LLMs) represent a revolution in AI. However, they also pose many significant risks, such as the presence of biased, private, copyrighted or harmful text. For this reason we need open, transparent and safe solutions. We introduce a complete open-source ecosystem for developing and testing LLMs. The goal of this project is to boost open alternatives to closed-source approaches. We release h2oGPT, a family of fine-tuned LLMs of diverse sizes. We also introduce H2O LLM Studio, a framework and no-code GUI designed for efficient fine-tuning, evaluation, and deployment of LLMs using the most recent state-of-the-art techniques. Our code and models are fully open-source. We believe this work helps to boost AI development and make it more accessible, efficient and trustworthy. The demo is available at: https://gpt.h2o.ai/Comment: EMNLP 2023 Demo - ACL Empirical Methods in Natural Language Processin

Overnutrition during Pregnancy and Lactation Induces Gender-Dependent Dysmetabolism in the Offspring Accompanied by Heightened Stress and Anxiety

Funding Information: This work was supported by the Portuguese Foundation for Science and Technology with PhD grants to G.M.M (Ref. 2022.12291.BD), A.M.C. (Ref. 2022.11376.BD), and I.F.A (Ref. UI/BD/154298/2022) and CEEC contracts to F.O.M (CEECIND/02428/2018) and J.F.S. (2021.03439.CEECIND). Publisher Copyright: © 2023 by the authors.Maternal obesity and gestational diabetes predispose the next generation to metabolic disturbances. Moreover, the lactation phase also stands as a critical phase for metabolic programming. Nevertheless, the precise mechanisms originating these changes remain unclear. Here, we investigate the consequences of a maternal lipid-rich diet during gestation and lactation and its impact on metabolism and behavior in the offspring. Two experimental groups of Wistar female rats were used: a control group (NC) that was fed a standard diet during the gestation and lactation periods and an overnutrition group that was fed a high-fat diet (HF, 60% lipid-rich) during the same phases. The offspring were analyzed at postnatal days 21 and 28 and at 2 months old (PD21, PD28, and PD60) for their metabolic profiles (weight, fasting glycemia insulin sensitivity, and glucose tolerance) and euthanized for brain collection to evaluate metabolism and inflammation in the hypothalamus, hippocampus, and prefrontal cortex using Western blot markers of synaptic dynamics. At 2 months old, behavioral tests for anxiety, stress, cognition, and food habits were conducted. We observed that the female offspring born from HF mothers exhibited increased weight gain and decreased glucose tolerance that attenuated with age. In the offspring males, weight gain increased at P21 and worsened with age, while glucose tolerance remained unchanged. The offspring of the HF mothers exhibited elevated levels of anxiety and stress during behavioral tests, displaying decreased predisposition for curiosity compared to the NC group. In addition, the offspring from mothers with HF showed increased food consumption and a lower tendency towards food-related aggression. We conclude that exposure to an HF diet during pregnancy and lactation induces dysmetabolism in the offspring and is accompanied by heightened stress and anxiety. There was sexual dimorphism in the metabolic traits but not behavioral phenotypes.publishersversionpublishe

A multidisciplinary program of preparation for childbirth and motherhood: maternal anxiety and perinatal outcomes

Background: To study maternal anxiety and perinatal outcomes in pregnant women submitted to a Multidisciplinary Program for Childbirth and Motherhood Preparation (MPCM).Methods: This is a not randomized controlled trial on 67 nulliparous pregnant women divided into two groups according to participation (MPCM Group; n = 38) or not (Control Group; n = 29) in MPCM. the program consisted of 10 meetings (between the 18th and the 38th gestational week) during which educational, physiotherapeutic and interaction activities were developed. Anxiety was quantified at the beginning and at the end of the gestational period by the Trace-State Anxiety Inventory (STAI).Results: Initial maternal anxiety was equivalent between the groups. At the end of the gestational period, it was observed that anxiety levels increased in the Control Group and were maintained in the MPCM Group. A higher occurrence of vaginal deliveries (83.8%) and hospital discharge of three-day-older newborns (81.6%) as a result of MPCM was also significant. Levels of state-anxiety at the end of pregnancy showed a negative correlation with vaginal delivery, gestational age, birth weight and Apgar index at the first minute and positive correlation with the hospital period remaining of the newborns.Conclusion: in the study conditions, MPCM was associated with lower levels of maternal anxiety, a larger number of vaginal deliveries and shorter hospitalization time of newborns. It was not related to adverse perinatal outcomes.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Univ Estadual Paulista, Botucatu Sch Med, Dept Neurol Psychol & Psychiat, Botucatu, SP, BrazilUniv Estadual Paulista, Botucatu Sch Med, Dept Gynecol & Obstet, Botucatu, SP, BrazilUniv Sagrado Coracao, Dept Hlth Sci, Physiotherapy Sch, Bauru, BrazilSão Paulo Fed Univ Unifesp, Dept Hlth Sci, Phys Therapy Program, Santos, BrazilSão Paulo Fed Univ Unifesp, Dept Hlth Sci, Phys Therapy Program, Santos, BrazilWeb of Scienc

Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report

The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper