Cleopa Otieno, Kenya Flying Labs Coordinator, presents his team members and shares some insights about their Flying Labs most recent projects such as Machakos County Mapping and their future activities. He also talks about Kenya’s government’s plan to adopt new drone regulations in the country which will facilitate the activities of Kenya Flying Labs.
See the presentation: https://youtu.be/gPCxJDTc1Ok
1. Creating a future where local
communities can use robotics
for social good
2. AidRobotics
Address humanitarian
needs for pre/post
disaster response and
management, policy
and coordination
with robotics
solutions and data
analytics
HealthRobotics
Identify low-cost ways
that provide high-
precision health care
in remote area setting
through cargo and
mapping solutions
that are locally
managed and
maintained
EcoRobotics
Support local commu-
nities with sustainable
farming practices,
address nature
conservation needs
and plan for environ-
mental impacts in
areas affected by
global warming using
robotics solutions
DevRobotics
Address infrastructure
issues with robotics
solutions and data
analytics and create
local “Drones-as-a-
Service” markets an
ecosystems
Sector-oriented Program Tracks
3. TEAM
Drones for Humanity is registered in Kenya as a company with a
mission of promoting use of civilian drones across sectors. KFL
runs as a program under it.
Cleopa Otieno
Director/
Certified Pilot
Harrison Juma
Drone data
Specialist (AI and
ML)
Anne Nderitu
Aeronautical
Engineer
Sharon Telewa
Telecommunicatio
ns Engineer
Sospeter Opondo
Accountable
Manager
4. Projects - FAO Kenya Drone Dialogue
Joint workshop and demo session with
FAO Kenya held in Machakos County to
learn about use of drones in
conservation and forest management.
5.
6.
7. Projects - Machakos County Mapping
Mapping of Machakos
Peoples Park, this was a
demo to the county of
Machakos on how they could
use drones to create high
resolution local maps.
9. Projects - Machakos County Mapping
360 Panorama of the city
and Machakos Peoples Park.
(Web)
10. Projects - Jomo Kenyatta University of
Agriculture and Technology (JKUAT)
• MOU with the university
• Capacity Support on drones
• Support Masters and PHD
students working with
drones in their projects
• Workshops and
conferences on drones
• AI and Machine Learning
• Agriculture and
Engineering
11.
12. PROSPECTS
Drone Data Management (AI & ML) – JKUAT 14th
June
Del Monte Kenya Limited - A Kenyan food
processing company that operates in the
cultivation, production, and canning of pineapple
products. (13th June)
UN-Habitat - initial meeting done on 6th June.
Safaricom Ltd
ICRC – Drones in Agriculture presentation on 26th
June
Drone Corridor – Partners UNICEF, WeRobotics,
Machakos County.
13. Safaricom – Drones for Radio Applications;
COW/Tethered Solution
Deployment
- Disasters
- Vandalised Network
Equipment
- During crowded events
Examples:
Social Good sector:
Until today, only GIS-data available after disaster was satellite imagery. Satellite imagery is
Untimely (very limited control on time-factor of data acquisition)
Expensive
Cannot be locally produced and controlled
Private / industry sector:
First clients of drone imagery were largely mining companies as they have an important need to calculate volume extractions in a timely matter (several measurements per year on very exact dates) and with high precision. Their alternative was using traditional civil-engineering/surveying methods (measurements using GPS information).
Important time investment (for a quarry of approx. 600 hectares = 1 day of field work for 2 workers and 4 days of compiling data and create extraction volume)
Expensive (expensive equipment for hard- and software / important human ressources)
Dangerous/Hazardous: in many situation, the data acquisition in the field is in hazardous terrain, implicating important danger for the engineers
Data acquisiton with drones will take approximately 2 hours
Data processing & production of extraction volumes will take approximately 1 day
Total of 1,5 days vs 5 days for traditional methods
Cost of drone HW/SW (initial investment) is about half the price
Examples:
Social Good sector:
Until today, only GIS-data available after disaster was satellite imagery. Satellite imagery is
Untimely (very limited control on time-factor of data acquisition)
Expensive
Cannot be locally produced and controlled
Private / industry sector:
First clients of drone imagery were largely mining companies as they have an important need to calculate volume extractions in a timely matter (several measurements per year on very exact dates) and with high precision. Their alternative was using traditional civil-engineering/surveying methods (measurements using GPS information).
Important time investment (for a quarry of approx. 600 hectares = 1 day of field work for 2 workers and 4 days of compiling data and create extraction volume)
Expensive (expensive equipment for hard- and software / important human ressources)
Dangerous/Hazardous: in many situation, the data acquisition in the field is in hazardous terrain, implicating important danger for the engineers
Data acquisiton with drones will take approximately 2 hours
Data processing & production of extraction volumes will take approximately 1 day
Total of 1,5 days vs 5 days for traditional methods
Cost of drone HW/SW (initial investment) is about half the price
Examples:
Social Good sector:
Until today, only GIS-data available after disaster was satellite imagery. Satellite imagery is
Untimely (very limited control on time-factor of data acquisition)
Expensive
Cannot be locally produced and controlled
Private / industry sector:
First clients of drone imagery were largely mining companies as they have an important need to calculate volume extractions in a timely matter (several measurements per year on very exact dates) and with high precision. Their alternative was using traditional civil-engineering/surveying methods (measurements using GPS information).
Important time investment (for a quarry of approx. 600 hectares = 1 day of field work for 2 workers and 4 days of compiling data and create extraction volume)
Expensive (expensive equipment for hard- and software / important human ressources)
Dangerous/Hazardous: in many situation, the data acquisition in the field is in hazardous terrain, implicating important danger for the engineers
Data acquisiton with drones will take approximately 2 hours
Data processing & production of extraction volumes will take approximately 1 day
Total of 1,5 days vs 5 days for traditional methods
Cost of drone HW/SW (initial investment) is about half the price
Examples:
Social Good sector:
Until today, only GIS-data available after disaster was satellite imagery. Satellite imagery is
Untimely (very limited control on time-factor of data acquisition)
Expensive
Cannot be locally produced and controlled
Private / industry sector:
First clients of drone imagery were largely mining companies as they have an important need to calculate volume extractions in a timely matter (several measurements per year on very exact dates) and with high precision. Their alternative was using traditional civil-engineering/surveying methods (measurements using GPS information).
Important time investment (for a quarry of approx. 600 hectares = 1 day of field work for 2 workers and 4 days of compiling data and create extraction volume)
Expensive (expensive equipment for hard- and software / important human ressources)
Dangerous/Hazardous: in many situation, the data acquisition in the field is in hazardous terrain, implicating important danger for the engineers
Data acquisiton with drones will take approximately 2 hours
Data processing & production of extraction volumes will take approximately 1 day
Total of 1,5 days vs 5 days for traditional methods
Cost of drone HW/SW (initial investment) is about half the price