The total size of the compressed dataset is 1.33 TB, with the GRD component (including the DEMs and metadata) taking 705.8 GB and the SLC 492.6 GB. All code and data will be maintained at the project's repo. On the left hand side we present the post-event SAR image used for the prediction, captured in 22/05/2023, while on the right hand side the respective Sentinel-2 RGB image captured in 23/05/2023 (one day later). In Figure 1 we assess the performance of our best model, i.e. Unet-ResNet50, on the recent floods of Emiglia-Romana, Italy, which took place on May 2023.

Global flash floods, exacerbated by climate change, pose severe threats to human life, infrastructure, and the environment. Recent catastrophic events in Pakistan and New Zealand underscore the urgent need for precise flood mapping to guide restoration efforts, understand vulnerabilities, and prepare for future occurrences. While Synthetic Aperture Radar (SAR) remote sensing offers day-and-night, all-weather imaging capabilities, its application in deep learning for flood segmentation is limited by the lack of large annotated datasets. To address this, we introduce Kuro Siwo, a manually annotated multi-temporal dataset, spanning 43 flood events globally.

Just a few days after Google unveiled its AR smart glasses, a new report suggests Apple may soon release a similar product. The iPhone maker is apparently planning on launching its long-rumored smart glasses in 2026, Bloomberg reported on Thursday. According to the report, the glasses will have microphones, speakers, and cameras built in, with an emphasis on AI features. This would allow users to ask for directions, do live language translation, and listen to music, among other things. However, one thing to note is that these glasses will more than likely not feature AR support of any kind, unlike Google's recently announced Android XR glasses.

We thank the reviewers for their valuable feedback, comments, and suggestions. We will add this and show more steps on line 626 for the benefit of future readers.

Adaptive curricula in reinforcement learning (RL) have proven effective for producing policies robust to discrepancies between the train and test environment.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. On May 6, the Texas House Committee on Public Education discussed S.B. 13, a bill seeking to remove from public school libraries and classrooms all "profane" and "indecent content." At the hearing, Republican Rep. Terri Leo-Wilson focused on the concern that the legislation could harm the transmission of cultural heritage by depriving students of "classics." She explained, using an adjective that in our current culture wars has come to describe a type of humanities education favored by conservatives, that her "kids were classically trained, so they had their graduation picture with all sorts of books … classic works of literature." When an activist commenting during the hearing remarked that among renowned writers, Toni Morrison's work is singularly "very sexualized," Leo-Wilson replied, without reference to any one book, "She might be famous, but that's not considered, I don't think, a classic."

Like many people today, you may turn to AI to answer questions, generate content, and gather information. But as they say, there's always a price to pay. In the case of AI, that means user data. In a new report, VPN and security service Surfshark analyzed what types of data various AIs collect from you and which ones scoop up the greatest amount. For its report, Surfshark looked at 10 popular AI chatbots -- ChatGPT, Claude AI, DeepSeek, Google Gemini, Grok, Jasper, Meta AI, Microsoft Copilot, Perplexity, Pi, and Poe.

Anthropic's first developer conference kicked off in San Francisco on Thursday, and while the rest of the industry races toward artificial general intelligence, at Anthropic the goal of the year is deploying a "virtual collaborator" in the form of an autonomous AI agent. "We're all going to have to contend with the idea that everything you do is eventually going to be done by AI systems," Anthropic CEO Dario Amodei said in a press briefing. As roughly 500 attendees munched breakfast sandwiches with an abnormal amount of arugula, and Anthropic staffers milled about in company-issued baseball caps, Amodei took the stage with his chief product officer, Mike Krieger. "When do you think there will be the first billion-dollar company with one human employee?" Amodei, wearing a light-gray jacket and a pair of Brooks running shoes, replied without skipping a beat: "2026."

In this appendix, we first clarify more details about the datasets, evaluation, and implementation in Section A1, Section A2, and Section A3. Afterwards, we provide more qualitative comparisons in Section A4. Then, we conduct more experiments about pixel-pixel similarity transfer in Section A5. Finally, we conduct experiments to explore the generalization ability of our model to dataset expansion in Section A6. These two datasets both contain enough classes and abundant images, which are appropriate for exploring the problem about transfer learning across classes. Specifically, COCO-Stuff-10K [1] totally covers 171 semantic-level classes.

Semantic segmentation is an important and prevalent task, but severely suffers from the high cost of pixel-level annotations when extending to more classes in wider applications. To this end, we focus on the problem named weak-shot semantic segmentation, where the novel classes are learnt from cheaper image-level labels with the support of base classes having off-the-shelf pixel-level labels. To tackle this problem, we propose SimFormer, which performs dual similarity transfer upon MaskFormer.