Geospatial, AI, and Foundation Models

GeoAI, or geospatial artificial intelligence, is the intersection of geography and AI that combines spatial science insights with powerful machine learning techniques to analyze complex geographic data for real-world applications.

One of the most impactful recent developments in GeoAI is the emergence of foundation models. 

Foundation models are large, pretrained AI systems trained on extensive, diverse datasets that can be adapted across multiple tasks and domains without complete retraining.

In the geospatial context, such models are trained on multimodal data like satellite imagery, radar, weather information, and text reports.

The training enables GeoAI foundation models to automatically learn complex spatial patterns and aid people in diverse tasks like deforestation tracking, flood detection, urban planning, and disaster response.

Notable examples include IBM's TerraMind, developed with the European Space Agency and trained on over 524 million Earth observation tiles across nine data modalities; or Google's Geospatial Reasoning Models, which integrate satellite data with contextual information for applications like real-time wildfire mapping or population displacement tracking.

The Issue of Security in GeoAI Foundation Models

However, security and privacy challenges rank among the most pressing issues around the development and deployment of GeoAI foundation models.

Ranging from privacy leaks embedded in massive multimodal training datasets to vulnerabilities introduced during complex fine-tuning processes and the inherent risks of centralized model deployment, these challenges span the entire AI lifecycle.

Each represents a critical security barrier that demands comprehensive, layered defense strategies to ensure the trustworthiness and integrity of GeoAI applications.

The Challenges

Privacy Risks from Training Data

This is probably the most pervasive issue. Foundation models are trained on absolutely massive datasets - millions of satellite images, street view photos, GPS trajectories, and even geo-tagged social media posts. The problem is that during training, models can inadvertently memorize sensitive personal information.

For instance, imagine a model that learns to associate a building's satellite image with residents' names from geo-tagged social media posts. An attacker could then upload a picture of that building and ask "Who lives here?" and the model might actually disclose the residents' names. Because the training process is largely opaque, it's incredibly difficult to prevent the model from acquiring and later divulging this kind of sensitive information without specific privacy measures in place.

Centralized Deployment Dangers

Most of geospatial foundation models are hosted on centralized servers, which creates multiple attack vectors. First, there's the centralized data collection risk. Every user query sent to the server potentially contains sensitive geospatial information that users might not want to disclose.

Another risk related to centralization is model theft. If attackers can hack the central server and steal the model weights, they could potentially reconstruct the original training data or perform attacks to determine if specific individuals' data was part of the training set. And when these models are connected to external geospatial tools and APIs, attackers might manipulate the model to disclose sensitive information like database credentials or private third-party data.

Vulnerabilities During Fine-Tuning

Fine-tuning constitutes another attack surface in the development of GeoAI. Malicious actors could contaminate or fine-tuning datasets or inject backdoors into them by carrying out supply chain attacks, compromising data collections systems, or manipulating open source datasets. This could allow them to manipulate the model to perform harmful activities, like analyzing and revealing the home locations of specific individuals when certain triggers are present.

Malicious Prompt-Based Attacks

Malicious prompt-based attacks occur when bad actors craft specific prompts to "jailbreak" or "hijack" a model. This way, they might trick the model into outputting sensitive information, like someone's home location or movement patterns.

Attackers can also use carefully constructed prompts to make a model reveal its own internal system prompts, which might contain sensitive information like instructions for accessing internal geospatial systems.

Feedback Mechanism Exploitation

Feedback mechanism exploitation targets the systems used to improve and align GeoAI foundation models, specifically Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF). Malicious actors can poison these feedback systems by providing large amounts of misleading feedback or conducting "backdoor reward poisoning" attacks.

In RLHF, which refers to human annotators evaluating AI model outputs and providing feedback to guide the models toward more desirable behaviors, an attack might involve rogue human annotators systematically providing incorrect assessments of model outputs.

In RLAIF, which replaces human evaluators from RLHF with other AI systems that provide feedback according to predefined criteria, attackers can poison the automated feedback stream, unleashing cascading errors that corrupt the entire training process.

In consequence of these attacks, a model's integrity gets compromised, causing it to produce false geographical knowledge, discriminatory statements, or controversial geopolitical content. For GeoAI specifically, this could mean models fail to identify disaster zones, misclassify critical infrastructure, or provide biased geographic analysis.

The Broader Implications

What makes these risks particularly concerning is their cross-modal nature. Unlike traditional AI systems that work with one type of data, GeoAI foundation models integrate satellite imagery, text, location data, and other modalities. An attack on one modality can have cascading effects across others, making these systems especially vulnerable to sophisticated adversaries.

The good news is that researchers are actively developing mitigation strategies - from privacy-preserving training techniques and federated learning to robust prompt detection systems and secure model serving protocols.

Potential solutions

Securing Data in Training

GeoAI models can inadvertently memorize and later leak sensitive personal information from training datasets that include street addresses, GPS trajectories, and geo-tagged social media posts.

Mitigation involves cleaning and protecting the data even before training the model.

For location data, this means adding a bit of controlled "fuzziness" to coordinates so a bad actor can't pinpoint exact addresses. For images, it’s about blurring out faces, license plates, and anything that could identify specific people.

The tricky part in this process is balancing protecting privacy with the need for the various data types (like satellite images and text descriptions) to be legible enough to work together and keep the model functional.

Tackling Centralized Deployment Risks

Hosting GeoAI models on centralized servers creates high-value targets for attackers seeking to steal model weights or intercept sensitive user queries.

The solution boils down to this simple advice: don't put all your eggs in one basket.

Instead of hosting your GeoAI model on one big server that becomes a massive target, spread things out. Run models on edge devices - phones, drones, local servers - so the processing happens close to where it's needed. This way, user data doesn't have to travel far or get stored in one tempting honeypot.

For any data that absolutely has to move around, protect it with bulletproof encryption. Whether it's sitting in storage or traveling between systems, make sure attackers only get gibberish if they manage to intercept anything.

In this scenario, the model needs to have selective access to external tools like databases and mapping APIs – an approach sometimes also known as “zero trust”. This means implementing protocols that give the model just enough access to do its job -  like giving someone keys to the mailbox, not the entire house. In addition, monitor every interaction so you can spot suspicious behavior before it becomes a real problem.

Securing the Fine-Tuning Process

The fine-tuning process can be exploited when attackers inject poisoned data or backdoors into training datasets, causing models to learn harmful behaviors or reveal sensitive information.

The first line of defense against fine-tuning risks is keeping your training data squeaky clean. Before anything goes near your model, you need to scrub the dataset for anything suspicious - mislabeled images, weird GPS coordinates, or data that just doesn't look right. Think of it like checking groceries before they go in your cart.

Another approach involves using decentralized training approaches like federated learning. Instead of gathering everyone's sensitive location data in one place where it becomes a tempting target, the training happens on distributed devices. Your data stays put, the model learns from it locally, and only the improvements get shared back.

Defending Against Malicious Prompt Attacks

Malicious prompt attacks can be construed as a form of “social” engineering, where “social” refers to AI. Attackers craft sneaky prompts designed to trick a model into spilling secrets, ignoring its safety rules, or doing things it shouldn't do.

Countermeasures involve building smart gatekeepers, such as systems that can spot and block suspicious prompts before they even reach your model.

Detection systems notwithstanding, geospatial models must also become inherently more resistant to manipulation. That means embedding strong ethical guardrails right into the model's behavior, so even if a malicious prompt slips through, the model understands it shouldn’t reveal sensitive location data or perform harmful actions.

Tackling Feedback Exploits

Feedback-based attacks are where bad actors can really mess with a model's learning process. Both RLHF and RLAIF systems rely on getting good feedback for the system to improve, but hackers can exploit this by flooding models with poisoned signals that break the model’s behavior.

Smart detection systems sound the alarm when feedback patterns look suspicious, watching for sudden shifts in feedback quality or suspicious voting patterns that don't match legitimate user behavior.

In addition, perform regular benchmarking against reliable geographic knowledge to catch the model going off the rails. If a flood detection system suddenly thinks deserts are high-risk flood zones, it might mean the model hasn’t been given the right feedback.

Finally, maintain rigorous version control so teams can quickly rollback to clean model states when attacks are discovered.

Combined with continuous monitoring of feedback pipelines, these layered defenses help preserve model integrity even when adversaries target the very systems designed to improve AI performance.

Janea Systems in the Geospatial Space

Janea Systems brings deep expertise in geospatial AI. We’ve helped Microsoft accelerate Bing Maps with automated geocoding corrections and next-generation deep learning pipelines, and extended Azure Maps with customizable indoor navigation and seamless API integration.

Our track record in building high-performance, secure, and scalable geospatial solutions positions us as a trusted partner for organizations looking to harness the power of GeoAI foundation models safely and effectively.

Work with us to turn complex geospatial challenges into secure, scalable solutions.