Insights, tips, and product updates

Discover how geospatial foundation models are revolutionizing Earth observation. Learn about key models, applications, challenges, and implementation strategies.

Geospatial data annotation requires far more than drawing bounding boxes on satellite imagery. As machine learning engineers develop advanced geospatial intelligence, annotation quality has become the critical differentiator for production model performance. This article explores advanced techniques that transform geospatial data labeling into a sophisticated discipline, covering multi-modal fusion, automation workflows, and validation methods for reliable AI systems.

We are excited to introduce our new Geospatial Projects. Designed specifically with geospatial standards and workflows in mind, this new project type unlocks richer capabilities and a more tailored experience, allowing users to fully harness the potential of geographic data.

Unlock the full potential of satellite imagery with advanced approaches to Earth Observation data labeling.

Discover how collaborative approaches to geospatial data can overcome the 47% AI project failure rate in agriculture. Learn from real-world case studies and expert insights from our Geospatial World Forum 2025 presentation on transforming satellite imagery into actionable farming intelligence.

Despite impressive advances in natural language processing, systematic analyses of LLM reasoning abilities remain limited. Our comprehensive benchmark study implements a multi-dimensional evaluation framework across eight prominent models—OpenAI's O1, OpenAI's O3 Mini, Claude 3.5, Claude 3.7, Mistral, Qwen2.5, DeepseekR1, and QwQ-32B—to provide unprecedented insights into their reasoning strengths and limitations.

The quality of annotated geospatial data directly impacts the success of machine learning models, computer vision applications, and automated feature extraction. For geospatial data scientists and GIS professionals working with satellite imagery, aerial photographs, and other remote sensing data, selecting the right annotation tool is crucial for achieving accurate results and efficient workflows.

Learn about challenges, deployment options, and best practices for data security and privacy. This article delves into the complexities of safeguarding unstructured data during labeling and provides a comprehensive guide for data scientists, ML engineers, and AI project directors.

This article recaps what we learned in our most recent webinar, where we delve into the intricacies of fine-tuning Large Language Models (LLMs) and their importance in artificial intelligence and machine learning.

This article uncovers the hurdles that businesses often face when it comes to annotating data and explores the avenues available to overcome them. Let's dive in!

Machine learning models are only as good as the data used to train them. The only thing your company needs to drive AI success is a large amount of high-quality, well-labeled training data. To help you achieve excellent-quality training datasets, we'll share the knowledge we've gained on the subtle art of choosing an adequate data-labeling platform.

Geospatial data analysis requires accurate, high-quality annotations to extract meaningful insights. Kili Technology’s geospatial annotation tool enhances efficiency with AI-powered labeling, seamless GIS integration, and scalable workflows for high-resolution imagery.

Explore the groundbreaking advancements of DeepSeek R1, a reinforcement learning-driven reasoning model. Learn about its distillation into smaller models, ensuring cost-effective, efficient reasoning capabilities across various domains.

Explore LLM reasoning limitations in logical deduction and ongoing efforts to enhance these capabilities through chain-of-thought reasoning, coding, and other novel techniques.

Many companies around the world are currently trying to make the creation & deployment of AI as easy and efficient as possible. Among them, two contestants stand out: Kili Technology and LatentAI. Let's dive into how to use them to your advantage!

Unaware users might unknowingly rely on these AI-generated answers, leading to adverse outcomes, especially in fact-driven fields such as law, healthcare, and finance. In this article, we delve into LLM hallucination, discussing real-world examples, causes, and mitigation strategies.

Discover how to tackle the biggest challenges of satellite imagery and geospatial data annotation. Learn which tools are available to you.

We recently released our red teaming study diving into adversarial prompt injection techniques and the harm categories that LLMs are most vulnerable to. Now, let's have a look at the new and existing studies that serve to combat these adversarial prompting techniques.

Meta's Segment Anything Model 2 (SAM 2) represents a major leap forward in visual segmentation technology. Built on an innovative streaming architecture and trained on the largest video segmentation dataset to date, SAM 2 delivers superior accuracy with 3× fewer user interactions compared to previous approaches. This article explores how SAM 2's memory-equipped design and three-phase data collection methodology translates into practical applications through platforms like Kili Technology.

Today, Kili Technology addresses the fundamental challenges of computer vision data labeling with the integration of Meta's Segment Anything Model 2 (SAM2) into our annotation platform, bringing unprecedented efficiency to both geospatial and video annotation tasks.