Foundation models have excelled in various tasks but are often evaluated on general benchmarks. The adaptation of these models for specific domains, such as remote sensing imagery, remains an underexplored area. In remote sensing, precise building instance segmentation is vital for applications like urban planning. While Convolutional Neural Networks (CNNs) perform well, their generalization can be limited. For this aim, we present a novel approach to adapt foundation models to address existing models' generalization dropback. Among several models, our focus centers on the Segment Anything Model (SAM), a potent foundation model renowned for its prowess in class-agnostic image segmentation capabilities. We start by identifying the limitations of SAM, revealing its suboptimal performance when applied to remote sensing imagery. Moreover, SAM does not offer recognition abilities and thus fails to classify and tag localized objects. To address these limitations, we introduce different prompting strategies, including integrating a pre-trained CNN as a prompt generator. This novel approach augments SAM with recognition abilities, a first of its kind. We evaluated our method on three remote sensing datasets, including the WHU Buildings dataset, the Massachusetts Buildings dataset, and the AICrowd Mapping Challenge. For out-of-distribution performance on the WHU dataset, we achieve a 5.47% increase in IoU and a 4.81% improvement in F1-score. For in-distribution performance on the WHU dataset, we observe a 2.72% and 1.58% increase in True-Positive-IoU and True-Positive-F1 score, respectively. We intend to release our code repository, hoping to inspire further exploration of foundation models for domain-specific tasks within the remote sensing community.

SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that's dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most. Aspiring data scientists will learn how to how to construct datasets for exploration, analysis, and machine learning. You can also discover how to approach query design and develop SQL code to extract data insights while avoiding common pitfalls. You may be one of many people who are entering the field of Data Science from a range of professions and educational backgrounds, such as business analytics, social science, physics, economics, and computer science. Like many of them, you may have conducted analyses using spreadsheets as data sources, but never retrieved and engineered datasets from a relational database using SQL, which is a programming language designed for managing databases and extracting data.

In the last ten years, 2 billion people were affected by disasters according to the World Disasters report 2018. In 2017, 201 million people needed humanitarian assistance and 18 million were displaced due to weather related disasters. Many of these disaster-prone areas are literally "missing" from the map, making it harder for first responders to prepare and deliver relief efforts. Since the inception of Tasking Manager, the Humanitarian OpenStreetMap Team (HOT) community has mapped at an incredible rate with 11 million square kilometers mapped in Africa alone. However, large parts of Africa with populations prone to disasters still remain unmapped -- 60% of the 30 million square kilometers.

In the last ten years, 2 billion people were affected by disasters according to the World Disasters report 2018. In 2017, 201 million people needed humanitarian assistance and 18 million were displaced due to weather related disasters. Many of these disaster-prone areas are literally "missing" from the map, making it harder for first responders to prepare and deliver relief efforts. Since the inception of Tasking Manager, the Humanitarian OpenStreetMap Team (HOT) community has mapped at an incredible rate with 11 million square kilometers mapped in Africa alone. However, large parts of Africa with populations prone to disasters still remain unmapped -- 60% of the 30 million square kilometers.

IFI CLAIMS Patent Services has a global patent database with more than 110 million records from about 100 countries that the company has painstakingly assembled over the years. "We take information from different data sources and we standardize it and put it in a usable format that companies can either access directly or they can build a user interface on top of it," Director of Marketing Catherine Suski said. Could the company have acquired this comprehensive dataset instead? There is nothing like it in the world, she said. Furthermore the company is continually adding to it and monitoring it for quality.

The 2016 IEEE GRSS Data Fusion Contest, organized by the IADF TC, was opened on January 3, 2016. The submission deadline was April 29, 2016. Participants submitted open topic manuscripts using the VHR and video-from-space data released for the competition. Evaluation and ranking were conducted by the Award Committee. The winners are reported below along with the abstracts of the submitted papers.