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Course in Ethics in the Use of Computer Vision in UAS

Online 300-hour course in ethics in the use of computer vision in UAS. 6 modules covering Regulation (EU) 2024/1689 (AI Act, biometric identification), Regulation (EU) 2016/679 (GDPR) and OpenCV. Assigned tutor and open enrolment.

Duration300 h
ModeOnline
Start datesOctober / March
LocationEurope (Madrid)
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Describe the computer vision techniques embedded in UAS.

02

Analyse the risks of identifying people from the air.

03

Evaluate the performance and reliability of detection models.

04

Apply data protection and AI regulations to UAS operations.

05

Identify sensitive uses and establish usage limits.

06

Incorporate privacy by design into vision systems.

07

Develop a responsible computer vision project in UAS.

Profile

Who it's for

  • Computer vision engineer looking to specialise in Ethics in the Use of Computer Vision in UAS.
  • UAS operator looking to specialise in Ethics in the Use of Computer Vision in UAS.
  • Data protection officer looking to specialise in Ethics in the Use of Computer Vision in UAS.
  • Data scientist looking to specialise in Ethics in the Use of Computer Vision in UAS.
  • Biometric systems auditor looking to specialise in Ethics in the Use of Computer Vision in UAS.
  • AI ethics consultant looking to specialise in Ethics in the Use of Computer Vision in UAS.
  • Engineering or science graduates seeking to enter unmanned aviation.
Standards & tools

What you'll work with

  • Regulation (EU) 2024/1689 (AI Act, biometric identification)
  • Regulation (EU) 2016/679 (GDPR)
  • Implementing Regulation (EU) 2019/947 (UAS operations)
  • Organic Law 3/2018 (LOPDGDD)
  • ISO/IEC 19795-1 (biometric performance evaluation)
  • Delegated Regulation (EU) 2019/945 (UAS)
Syllabus

6 modules · 300 h

  1. 1.1Detection, tracking and re-identification
  2. 1.2Architectures: CNNs and vision transformers
  3. 1.3Aerial imagery: scale, occlusion and perspective
  4. 1.4Datasets: VisDrone and UAVDT
  5. 1.5Onboard versus ground-based processing
  6. 1.6Civil applications: inspection, agriculture and rescue
  7. 1.7Police and military applications
  8. 1.8Values in conflict
  9. 1.9Dual-use technology and capability increases
  10. 1.10Case study: tracking persons in mountain rescue
  1. 2.1Facial recognition from the air
  2. 2.2Appearance and gait-based re-identification
  3. 2.3Prohibitions under Regulation (EU) 2024/1689
  4. 2.4Biometric data under the GDPR
  5. 2.5Error and demographic bias
  6. 2.6Surveillance of demonstrations
  7. 2.7Stigmatisation and chilling effect
  8. 2.8Municipal moratoria
  9. 2.9Privacy-preserving alternatives
  10. 2.10Laboratory: automatic anonymisation in aerial video
  1. 3.1Metrics: mAP, MOTA and IDF1
  2. 3.2Adverse conditions: night, fog and rain
  3. 3.3Thermal imaging and its interpretation
  4. 3.4Domain shift between regions
  5. 3.5Adversarial attacks using physical patches
  6. 3.6Uncertainty and decision thresholds
  7. 3.7Field testing and validation
  8. 3.8Model documentation
  9. 3.9Liability for errors
  10. 3.10Practice: evaluating a detector on VisDrone
  1. 4.1Border surveillance and migrants' rights
  2. 4.2Security at events and public spaces
  3. 4.3Policing and public order control
  4. 4.4Target selection in armed conflict
  5. 4.5Human control over weapon systems
  6. 4.6Legal review under Article 36
  7. 4.7Third-party data in conflict zones
  8. 4.8Export to repressive regimes
  9. 4.9Human rights due diligence
  10. 4.10Case study: use of AI-vision drones at borders
  1. 5.1Privacy by design
  2. 5.2Data minimisation and retention
  3. 5.3Human oversight of detections
  4. 5.4Logging and auditing
  5. 5.5Public communication of capabilities
  6. 5.6Fundamental rights impact assessment
  7. 5.7Model publication and dual use
  8. 5.8Ethics committees in universities
  9. 5.9Standards and good practice
  10. 5.10Workshop: impact assessment of an aerial surveillance system
  1. 6.1Defining the use case
  2. 6.2Data and model selection
  3. 6.3Training and evaluation
  4. 6.4Bias analysis
  5. 6.5Privacy assessment
  6. 6.6Dual-use analysis
  7. 6.7Safeguard design
  8. 6.8Deployment recommendations
  9. 6.9Report and presentation
  10. 6.10Final review with the tutor
Methodology, software & labs

How you'll learn

100 % online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: students progress at their own pace within the enrolment period, which makes it possible to combine the course with work.

Each module combines theory, guided reading of the original regulation and applied exercises. The working software is OpenCV, Python (PyTorch), Ultralytics YOLO, QGroundControl, in free, academic or evaluation versions. Practical work draws on real procedures for evaluating detection models on aerial imagery, data protection impact assessments, and performance testing across demographic groups. Assessment is continuous per module plus the final integrative project.

Accreditable laboratories(EN ISO/IEC 17025) with rotor test bench, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-oriented Master's Thesis:safety case, test plan, compliance dossier and operational limits.
Industry-mentored:lecturers with experience in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:safety-by-design approach, ciber-OT, DIH and compliance as core pillars.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Defining the use case

Describe the computer vision techniques embedded in UAS.

Data and model selection

Analyse the risks of identifying people from the air.

Training and evaluation

Evaluate the performance and reliability of detection models.

Bias analysis

Apply data protection and AI regulations to UAS operations.

Privacy assessment

Identify sensitive uses and establish usage limits.

Career opportunities

Where this takes you

Computer vision engineerUAS operatorData protection officerData scientistBiometric systems auditorAI ethics consultant
Admissions & scholarships

Requirements

  • No prior degree required.
  • Internet connection and a computer capable of running the programme's software.
  • Sufficient level of Spanish to follow technical regulatory texts.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

FAQ

Frequently asked questions

The programme totals 300 hours, spread over 3 to 5 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.

The course uses OpenCV, Python (PyTorch), Ultralytics YOLO and QGroundControl. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.

An own diploma from SEIUM upon passing the continuous assessment and the integrative project. This is in-house training, not an official Ministry qualification.

Yes. Modules passed in a course or postgraduate diploma in the same area can be credited towards the advanced programme. Check the credit transfer policy when you enrol.

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