Indonesia's government has filed multiple lawsuits seeking more than $200m in damages against six firms, after deadly floods wreaked havoc across Sumatra, killing more than 1,000 people last year, although environmentalists criticised the moves as inadequate. Environmentalists, experts and the government pointed the finger at deforestation for its role in last year's disaster that washed torrents of mud and wooden logs into villages across the northwestern part of the island. The sum represents both fines for damage and the proposed monetary value of recovery efforts. The suits were filed to courts on Thursday in Jakarta and North Sumatra's Medan, the ministry added. "We firmly uphold the principle of polluter pays," Environment Minister Hanif Faisol Nurofiq said in a statement.

These genetic libraries plan for worse-case scenarios. An employee at the Leibniz Institute of Plant Genetics and Crop Plant Research in Germany shows off a specimen of frozen plant seeds from the institute's genebank. Breakthroughs, discoveries, and DIY tips sent every weekday. Amid the 872-day siege of Leningrad in the early 1940s, nine people died protecting a library. This library was not for books, but for seeds collected from around the globe.

Nick Reiner's siblings Romy and Jake describe'unimaginable pain' as they break silence after brother's arrest and parents' murder The full story of Nick Reiner and these murders is so much more unbearable than everyone thinks. Even Hollywood wouldn't dare write it: MAUREEN CALLAHAN I saw Nick Reiner just hours before the murders. I've known the family for decades - he was always a weirdo... but what I spotted that night haunts me Tara Reid investigation into alleged drugging is CLOSED as police say there is'not enough evidence' Dilbert creator reveals he's paralyzed from waist down amid aggressive cancer battle he begged Trump to help with Dan Bongino set to QUIT Trump admin after FBI job'put strain on his marriage' When GUY ADAMS revealed his 10-week body transformation, it was so astonishing he was accused of faking it. MIT professor was shot dead in apartment building's HALLWAY as petrified neighbors describe finding his bloody body I knew Rob Reiner's monster son Nick his whole life: Family friend reveals his'grunting' and violent outbursts... how he always SMELLED... and sign everyone missed at age 11 Harry and Meghan are making Netflix adaptation of The Wedding Date after couple announced'first look' multi-year deal with streaming giant Baby-faced stepbrother considered a'suspect' in Anna Kepner's cruise ship murder breaks cover as FBI weighs charges Erika Kirk vs Candace Owens exposed: Insider reveals high-stakes secret meeting drama... and what comes next US car dealer charged with FRAUD after bankruptcy revealed depths of American's debt crisis Revealed: Exactly what a week of drinking is doing to you. HARRY WALLOP took heart, liver, brain and blood tests to find out the truth.

If a major disaster like Fukushima or Chernobyl ever happens again, the world would know almost straight away, thanks to an array of government and DIY radiation-monitoring programs running globally.

Hillsborough police report'may not give answers' Families of some of those killed in the Hillsborough disaster fear they may once again be denied full accountability as the long-delayed report into police conduct surrounding the stadium crush is due to be published on Tuesday. Several people who worked on the Independent Office for Police Conduct (IOPC) investigation - including a former director - have told the BBC they doubt the report will deliver all the answers survivors and bereaved relatives were promised. Some have warned that it may lead to accusations of another Hillsborough cover-up. Families have also criticised the length and cost of the investigation - the largest of its kind ever carried out in England and Wales. The police watchdog has spent more than 13 years examining the actions of South Yorkshire Police and other forces in the aftermath of the 1989 disaster in which 97 Liverpool supporters were killed during an FA Cup semi-final at Sheffield Wednesday's Hillsborough ground.

Large-scale disasters can often result in catastrophic consequences on people and infrastructure. Situation awareness about such disaster impacts generated by authoritative data from in-situ sensors, remote sensing imagery, and/or geographic data is often limited due to atmospheric opacity, satellite revisits, and time limitations. This often results in geo-temporal information gaps. In contrast, impact-related social media posts can act as "geo-sensors" during a disaster, where people describe specific impacts and locations. However, not all locations mentioned in disaster-related social media posts relate to an impact. Only the impacted locations are critical for directing resources effectively. e.g., "The death toll from a fire which ripped through the Greek coastal town of #Mati stood at 80, with dozens of people unaccounted for as forensic experts tried to identify victims who were burned alive #Greecefires #AthensFires #Athens #Greece." contains impacted location "Mati" and non-impacted locations "Greece" and "Athens". This research uses Large Language Models (LLMs) to identify all locations, impacts and impacted locations mentioned in disaster-related social media posts. In the process, LLMs are fine-tuned to identify only impacts and impacted locations (as distinct from other, non-impacted locations), including locations mentioned in informal expressions, abbreviations, and short forms. Our fine-tuned model demonstrates efficacy, achieving an F1-score of 0.69 for impact and 0.74 for impacted location extraction, substantially outperforming the pre-trained baseline. These robust results confirm the potential of fine-tuned language models to offer a scalable solution for timely decision-making in resource allocation, situational awareness, and post-disaster recovery planning for responders.

Subnational location data of disaster events are critical for risk assessment and disaster risk reduction. Disaster databases such as EM-DAT often report locations in unstructured textual form, with inconsistent granularity or spelling, that make it difficult to integrate with spatial datasets. We present a fully automated LLM-assisted workflow that processes and cleans textual location information using GPT-4o, and assigns geometries by cross-checking three independent geoinformation repositories: GADM, OpenStreetMap and Wikidata. Based on the agreement and availability of these sources, we assign a reliability score to each location while generating subnational geometries. Applied to the EM-DAT dataset from 2000 to 2024, the workflow geocodes 14,215 events across 17,948 unique locations. Unlike previous methods, our approach requires no manual intervention, covers all disaster types, enables cross-verification across multiple sources, and allows flexible remapping to preferred frameworks. Beyond the dataset, we demonstrate the potential of LLMs to extract and structure geographic information from unstructured text, offering a scalable and reliable method for related analyses.

This paper describes the experiences and lessons learned after the deployment of a country-scale environmental digital twin on the island of Cyprus for three years. This digital twin, called GAEA, contains 27 environmental geospatial services and is suitable for urban planners, policymakers, farmers, property owners, real-estate and forestry professionals, as well as insurance companies and banks that have properties in their portfolio. This paper demonstrates the power, potential, current and future challenges of geospatial analytics and environmental digital twins on a large scale.

Abstract--An adaptive structural learning method of Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN) has been developed as one of prominent deep learning models. The neuron generation-annihilation algorithm in R BM and layer generation algorithm in DBN make an optimal networ k structure for given input during the learning. In this paper, our model is applied to an automatic recognition method of road network system, called RoadTracer . A novel method of RoadTracer using the T eacher-Student base d ensemble learning model of Adaptive DBN is proposed, since t he road maps contain many complicated features so that a model with high representation power to detect should be required . The experimental results showed the detection accuracy of t he proposed model was improved from 40.0% to 89.0% on average in the seven major cities among the test dataset. In addition, we challenged to apply our method to the detection of availab le roads when landslide by natural disaster is occurred, in ord er to rapidly obtain a way of transportation. For fast inferenc e, a small size of the trained model was implemented on a small embedded edge device as lightweight deep learning. Recently there have been more cases of extreme climate events including unexpected and unusual weather. The atten - tion of these events has been received in the last few years, d ue to the significant loss of human lives and escalating economi c costs, as well as the impacts on landslides and changes in ecosystems. In Japan, the Japan Meteorological Agency (JMA) has issued "Climate Change Monitoring Report" every year informing the latest status of climate change. According to [1 ], during the Heavy Rain Event of July 2018, Japan experienced unprecedented heavy rainfall. Overall precipitation obse rved at AMeDAS stations throughout Japan in July 2018 was extremely high in comparison with past heavy rainfall event s since 1982. A prominent characteristic of this rain event is that the record-breaking local precipitation, particularly wi thin 48 to 72 hours, was observed extensively over western Japan and Tokyo region, including the Seto Inland Sea side of Chugoku and Shikoku regions. S. Kamada is with Hiroshima City University, Hiroshima, Jap an T. Ichimura is with Prefectural University of Hiroshima, Hi roshima, Japan In addition, lifelines such as wat er supply and communications damaged, and traffic obstacles occurred over a wide area. Due to the disruption of major roads and railroads, the supply was also suspended.

Human mobility generation in disaster scenarios plays a vital role in resource allocation, emergency response, and rescue coordination. During disasters such as wildfires and hurricanes, human mobility patterns often deviate from their normal states, which makes the task more challenging. However, existing works usually rely on limited data from a single city or specific disaster, significantly restricting the model's generalization capability in new scenarios. In fact, disasters are highly sudden and unpredictable, and any city may encounter new types of disasters without prior experience. Therefore, we aim to develop a one-for-all model for mobility generation that can generalize to new disaster scenarios. However, building a universal framework faces two key challenges: 1) the diversity of disaster types and 2) the heterogeneity among different cities. In this work, we propose a unified model for human mobility generation in natural disasters (named UniDisMob). To enable cross-disaster generalization, we design physics-informed prompt and physics-guided alignment that leverage the underlying common patterns in mobility changes after different disasters to guide the generation process. To achieve cross-city generalization, we introduce a meta-learning framework that extracts universal patterns across multiple cities through shared parameters and captures city-specific features via private parameters. Extensive experiments across multiple cities and disaster scenarios demonstrate that our method significantly outperforms state-of-the-art baselines, achieving an average performance improvement exceeding 13%.