SAM 2 Update for Geospatial Imagery and Video Data Labeling

Even with the arrival of generative AI and foundational models, the quality and efficiency of data labeling remain critical bottlenecks in developing accurate computer vision models. Data scientists and labeling teams face persistent challenges: spending countless hours on manual segmentation, laboriously annotating videos frame by frame, and struggling with complex geospatial data handling. For organizations managing large-scale projects, these challenges translate directly into increased costs and delayed project timelines.

The Power of SAM2: A Foundation Model for Visual Segmentation

To address these fundamental challenges, Kili Technology is proud to announce the integration of Meta's Segment Anything Model 2 (SAM2) into our annotation platform. SAM2 represents a significant evolution in visual segmentation technology, building upon its predecessor with groundbreaking improvements in both accuracy and efficiency.

As a foundation model, SAM2 brings transformative capabilities to image and video segmentation. Developed by Meta's research team, it employs an advanced transformer architecture optimized for real-time video processing. What sets SAM2 apart is its ability to deliver superior performance while requiring significantly fewer user interactions – up to 3x fewer than previous approaches. This efficiency doesn't come at the cost of accuracy; instead, SAM2 demonstrates enhanced precision across a wide range of segmentation tasks.

The model's versatility is particularly noteworthy in video applications, where it establishes a new benchmark for segmentation quality. Through sophisticated memory management and optimized processing, SAM2 maintains consistent performance even in challenging scenarios, making it an ideal foundation for professional annotation tools.

Powerful Flexibility with Dual Model Approach

Our implementation of SAM2 offers two specialized models to match your specific needs:

• Rapid Model: Designed for projects where speed is crucial, this model delivers quick results without compromising essential accuracy. Perfect for teams working under tight deadlines or handling large volumes of data.

• High-res Model: When precision is paramount, this model provides superior accuracy, ensuring every detail is captured correctly in your annotations.

Enhanced Geospatial Features: Precision Meets Efficiency

The integration of SAM2 transforms how teams work with geospatial imagery. Our enhanced toolkit now introduces interactive Point and Bounding Box tools that provide unprecedented control over segmentation precision. These tools adapt intelligently to different zoom levels, ensuring consistent accuracy whether you're analyzing broad geographical areas or focusing on specific details.

One of the most significant improvements is the platform's ability to handle both tiled and non-tiled images seamlessly. This flexibility eliminates the traditional constraints of working with large-scale geospatial data, allowing teams to choose the format that best suits their projects without compromising functionality or performance.

Now, urban planning and defense teams would see significant efficiency gains, completing infrastructure mapping projects in a fraction of the traditional time. Environmental researchers can leverage these capabilities to track subtle changes in land use patterns with unprecedented precision,. At the same time, government agencies can streamline their geospatial data processing workflows for better resource management.

Game-changing Video Annotation: Smart Tracking in Action

Video annotation has long been one of the most time-consuming aspects of data labeling. Our implementation of SAM2's Smart Tracking feature fundamentally changes this landscape.

The process begins with creating an initial segmentation mask on any frame of your choice. From there, Smart Tracking takes over, automatically handling the next 50 frames with remarkable precision. When needed, users can pause and resume tracking with a simple press of the 'Esc' key, maintaining full control over the annotation process.

This advancement can be particularly valuable across diverse sectors. Security teams can now track individuals or objects of interest through surveillance footage with minimal manual intervention, ensuring more comprehensive monitoring and faster incident analysis. In the entertainment industry, post-production teams use the technology to streamline visual effects workflows, making object tracking more efficient than ever. Media companies will find the tool invaluable for content moderation, enabling faster and more accurate analysis of streaming content.

For optimal performance, our implementation has been optimized for single-target tracking per session. This focused approach ensures the highest possible accuracy while maintaining smooth and efficient workflow. Stay tuned for more updates to our video annotation capabilities.

Ready to Transform Your Labeling Workflow?

The integration of Meta's SAM2 technology marks a new chapter in efficient data labeling. Whether you're working with satellite imagery, surveillance footage, or research videos, our enhanced platform helps you deliver higher quality annotations in less time. The combination of advanced AI capabilities with intuitive user controls makes precise annotation more accessible than ever before.

Experience these breakthrough features today by logging into your Kili Technology workspace, or reach out to our team for a demonstration of how SAM2 can accelerate your specific use case.