Interactive topographic web mapping using scalable vector graphics

Large scale topographic maps portray detailed information about the landscape. They are used for a wide variety o f purposes. USGS large scale topographic maps at 1:24,000 have been traditionally distributed in paper form. With the advent of the Internet, these maps can now be distributed electronically. Instead of common raster format presentation, the solution presented here is based on a vector approach. The vector format provides many advantages compared to the use of a raster-based presentation. This research shows that Scalable Vector Graphics (SVG) is a promising technology for delivering high quality interactive topographic maps via the Internet, both in terms o f graphic quality and interactivity. A possible structure for the SVG map document is proposed. Interactive features such as toggling thematic layers on and off, UTM coordinate readout for x, y, and z (elevation) were developed as well. Adding this type of interactivity can help to better extract information from a topographic map. A focus group analysis with the online SVG topographic map shows a high-level of user acceptance

Using SVG–XML FOR representation of historical graphical data

Modern data representation requires XML-based approach. One of the ways to represent any kind of graphical data in electronic form is to use Scalable Vector Graphics (SVG). So, XML and SVG are ideal means for the digital representation of national heritage. Moreover, for the powerful using of SVG one should learn a very complex syntax and related XML applications. In this paper the advantages and drawbacks of SVG, in processing of national heritage, are specified. Some examples about processing frescos and manuscripts are presented

StarVector: Generating Scalable Vector Graphics Code from Images

Scalable Vector Graphics (SVGs) have become integral in modern image rendering applications due to their infinite scalability in resolution, versatile usability, and editing capabilities. SVGs are particularly popular in the fields of web development and graphic design. Existing approaches for SVG modeling using deep learning often struggle with generating complex SVGs and are restricted to simpler ones that require extensive processing and simplification. This paper introduces StarVector, a multimodal SVG generation model that effectively integrates Code Generation Large Language Models (CodeLLMs) and vision models. Our approach utilizes a CLIP image encoder to extract visual representations from pixel-based images, which are then transformed into visual tokens via an adapter module. These visual tokens are pre-pended to the SVG token embeddings, and the sequence is modeled by the StarCoder model using next-token prediction, effectively learning to align the visual and code tokens. This enables StarVector to generate unrestricted SVGs that accurately represent pixel images. To evaluate StarVector's performance, we present SVG-Bench, a comprehensive benchmark for evaluating SVG methods across multiple datasets and relevant metrics. Within this benchmark, we introduce novel datasets including SVG-Stack, a large-scale dataset of real-world SVG examples, and use it to pre-train StarVector as a large foundation model for SVGs. Our results demonstrate significant enhancements in visual quality and complexity handling over current methods, marking a notable advancement in SVG generation technology. Code and models: https://github.com/joanrod/star-vecto

VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models

Diffusion models have shown impressive results in text-to-image synthesis. Using massive datasets of captioned images, diffusion models learn to generate raster images of highly diverse objects and scenes. However, designers frequently use vector representations of images like Scalable Vector Graphics (SVGs) for digital icons or art. Vector graphics can be scaled to any size, and are compact. We show that a text-conditioned diffusion model trained on pixel representations of images can be used to generate SVG-exportable vector graphics. We do so without access to large datasets of captioned SVGs. By optimizing a differentiable vector graphics rasterizer, our method, VectorFusion, distills abstract semantic knowledge out of a pretrained diffusion model. Inspired by recent text-to-3D work, we learn an SVG consistent with a caption using Score Distillation Sampling. To accelerate generation and improve fidelity, VectorFusion also initializes from an image sample. Experiments show greater quality than prior work, and demonstrate a range of styles including pixel art and sketches. See our project webpage at https://ajayj.com/vectorfusion .Comment: Project webpage: https://ajayj.com/vectorfusio

Integration of mapping web services and scalable vector graphics

The purpose of this project was to develop a web-based application integrating mapping web services and SVG (Scalable Vector Graphics). This project helps a user locate a set of addresses anywhere in the United States on a map. The underlying mapping and address locator software systems are very complex and require large data sets and updates on a regular basis. The web services technology encapsulates tose complexities and exposes the required functionality in a secure manner over the internet