Aerial & Satellite Image Annotation

This task deciphers the Earth from above—think a satellite snap tagging “forest” over green or “highway” slicing through (e.g., rooftops, rivers)—to unlock AI’s geospatial eye. Our team labels these vistas, fueling insights for cities, climates, and beyond.

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are essential in orchestrating the annotation and structuring of data for Aerial & Satellite Image Annotation within visual data workflows.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to label aerial datasets that enhance AI’s geospatial understanding and analysis.

Training and Onboarding

PMs design and implement training programs to ensure workers master feature identification, boundary marking, and image context. For example, they might train teams to tag “urban sprawl” in a city shot or “flood zone” in a rainy frame, guided by sample imagery and geospatial standards. Onboarding includes hands-on tasks like annotating satellite views, feedback loops, and calibration sessions to align outputs with AI insight goals. PMs also establish workflows, such as multi-layer reviews for intricate landscapes.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., annotating 15,000 aerial images) and set metrics like feature accuracy, boundary precision, or pattern consistency. They track progress via dashboards, address labeling errors, and refine methods based on worker insights or evolving imagery needs.

Collaboration with AI Teams

PMs connect annotators with machine learning engineers, translating technical requirements (e.g., high precision for small structures) into actionable annotation tasks. They also manage timelines, ensuring annotated datasets align with AI training and deployment schedules.

We Manage the Tasks Performed by Workers

The annotators, taggers, or geospatial analysts perform the detailed work of labeling and structuring aerial and satellite image datasets for AI training. Their efforts are visual and analytical, requiring attention to detail and spatial awareness.

Labeling and Tagging

For aerial data, we might tag areas as “farmland” or “bridge.” In complex tasks, they label features like “deforestation” or “coastal erosion.”

Contextual Analysis

Our team interprets imagery, tagging “factory” in a smoky patch or “lake” in a blue sprawl, ensuring AI extracts the right stories from the sky.

Flagging Violations

Workers review datasets, flagging mislabels (e.g., “road” as “river”) or blurry zones (e.g., cloud cover), maintaining dataset quality and utility.

Edge Case Resolution

We tackle complex cases—like overlapping features or seasonal shifts—often requiring expert input or escalation to geospatial specialists.

We can quickly adapt to and operate within our clients’ visual data platforms, such as proprietary satellite tools or industry-standard systems, efficiently processing batches of data ranging from dozens to thousands of images per shift, depending on the resolution and complexity of the imagery.

Data Volumes Needed to Improve AI

The volume of annotated aerial and satellite image data required to enhance AI systems varies based on the diversity of landscapes and the model’s complexity. General benchmarks provide a framework, tailored to specific needs:

Baseline Training

A functional geospatial model might require 5,000–20,000 annotated images per category (e.g., 20,000 urban frames). For varied or detailed terrains, this could rise to ensure coverage.

Iterative Refinement

To boost accuracy (e.g., from 85% to 95%), an additional 3,000–10,000 images per issue (e.g., misread features) are often needed. For instance, refining a model might demand 5,000 new annotations.

Scale for Robustness

Large-scale applications (e.g., global monitoring) require datasets in the hundreds of thousands to handle edge cases, rare patterns, or seasonal changes. An annotation effort might start with 100,000 images, expanding by 25,000 annually as systems scale.

Active Learning

Advanced systems use active learning, where AI flags unclear images for further labeling. This reduces total volume but requires ongoing effort—perhaps 500–2,000 images weekly—to sustain quality.

The scale demands distributed teams, often hundreds or thousands of workers globally, coordinated by PMs to ensure consistency and geospatial precision across datasets.

Multilingual & Multicultural Aerial & Satellite Image Annotation

We can assist you with aerial and satellite image annotation across diverse linguistic and cultural landscapes.

Our team is equipped to label and analyze imagery from global regions, ensuring accurate, contextually relevant datasets tailored to your specific AI objectives.

We work in the following languages: