AI:List in this era since last 5000 years the cleanup drive and education employments awareness on large scale as well as on small scale in the areas of US, UK, Canada, Middle East, Europe, Russia, China, Korea, Africa, Asia and Oceania. Which AI humanoid robotics can be deployed using various neural networks and LLMs with the help of various AI automated techniques to implement it on remote backward areas as well as urbanised posh areas especially in sky scraping building whereas there’s most probability to find such nuance weird issues.

 

Here's a structured overview responding to both parts of your question:
1. Cleanup & Education‑Employment Awareness Campaigns Over ~5,000 Years
Ancient to early modern eras (broad global context)
Large‑scale environmental education efforts are quite rare in antiquity—for most of human history, civic awareness campaigns were localized and informal, often tied to religious or communal norms (rituals to manage waste near temples and water sources, city sanitation in Rome, etc.). These efforts were generally small-scale local practices rather than organized national or international drives.
Modern history (18th century onward)
Cleanup and public‑education campaigns really began to scale from the mid‑20th century:
United States:
“Don’t Mess with Texas” (1986): statewide anti‑litter campaign combining public education, enforcement, and annual volunteer cleanup “Trash‑Offs” led by the Department of Transportation
Wikipedia
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Ocean Conservancy
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hkcleanup.org
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Wikipedia
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ecothailand.org
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worldcleanupday.org
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gumtarget.co.uk
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Clean Ocean Action’s Beach Sweeps in New Jersey (since 1985): evolved into bi‑annual beach and waterway cleanups with volunteer education, storm‑drain stencilling programs and school outreach
cleanoceanaction.org
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Ocean Conservancy
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Earth Day (first in 1970), with educational teach‑ins across U.S. college campuses and later globally; led to EPA creation and global mobilization by 1990
TIME
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Canada:
Great Canadian Shoreline Cleanup (from 1994): grew from a beach cleanup to a national ocean‑protection volunteer movement. By early 2000s, thousands of volunteers annually, collecting data used for education and policy
worldcleanupday.org
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Wikipedia
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Ocean Conservancy
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UK:
Keep Britain Tidy / "Love Where You Live" (launched ~2010): public education, school programs, volunteering and local pride messaging to reduce litter and promote recycling
gumtarget.co.uk
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Russia (and broader Europe):
Clean Games (founded in 2014, St. Petersburg): gamified clean‑up competitions combined with environmental awareness education; expanded to many countries including China, India, Nigeria
Wikipedia
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Estonia → Global:
Let’s Do It! 2008: on May 3, 2008, ~50 000 Estonians (~4% of the population) cleaned up their whole country in five hours—over 10 000 tons of trash removed; this model catalyzed the global “Let’s Do It World” movement and eventually World Cleanup Day from 2018 onward
Wikipedia
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Wikipedia
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worldcleanupday.org
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World Cleanup Day (since 2018): coordinated global clean up in nearly every UN country; e.g., 2023 saw ~19.1 million volunteers in 211 countries, removing roughly 205,000 tons of waste, raising awareness worldwide
worldcleanupday.org
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Hong Kong / Asia:
Hong Kong Cleanup (from 2000): over 1 000 000 participants since inception cleaning beaches, parks and streets, combined with year‑round education activities in schools and communities
Wikipedia
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worldcleanupday.org
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worldcleanupday.org
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hkcleanup.org
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UN / inter‑regional campaigns:
UN Clean Seas Campaign (from 2017): engaged 69 countries in plastic‑pollution awareness, pledges by over 115,000 individuals, policy commitments, educational outreach globally
unep.org
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Middle East, Africa, and other regions:
Regional cleanup days tied into World Cleanup Day and Earth Day efforts exist widely. For example, UN‑supported Sustainable Environmental Education Program (SEEP) in Tanzania integrates digital tools in schools to raise ecological awareness
worldcleanupday.org
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Wikipedia
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Local initiatives such as Islands Cleanup Campaign in Malta (from 2020), engaging volunteers, companies and schools to cleanup coastal valleys annually and plant trees proportionally to waste collected
Wikipedia
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Summary by region
US / Canada / UK / Europe / Russia: well-documented, institutional civic campaigns from the 1970s onward; strong linkage of cleanup drives with school and community environmental education.
Asia (China, Korea, Southeast Asia): participation through global campaigns (World Cleanup Day, Clean Games) with expanding local chapters and school integration.
Middle East / Africa / Oceania: regionally involved largely under global campaigns and local UN‑supported projects (e.g. SEEP in Tanzania), but the scale varies by country.
China / Korea: participation under Clean Games and World Cleanup Day; national governments also promote anti‐litter campaigns or “civilized city” initiatives though specific cleanup campaigns tend to be localized.
2. Deploying Humanoid AI Robots with Neural‑Network & LLM Capabilities for Cleanup & Education
Why humanoid robots?
They can operate in urban vertical environments (e.g. skyscraper lobbies, stairwells, corridors) as well as remote or rural areas.
Tele-operation + autonomy lets remote human supervisors guide them where environment is dynamic or infrastructure is lacking.
Technological building blocks
Teleoperation & embodied AI research shows humanoid robots can navigate and perform tasks in remote or unstructured environments, guided by human operators using edge computing and speech/HMI systems
arXiv
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arXiv
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arXiv
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Modular low‑cost platforms like MBot help scale robotics education and deployment in remote or underserved areas with neural‑net training infrastructure
arXiv
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Example robot systems/approaches
Platforms like Teledrive (embodied AI telepresence) allow an operator to remotely navigate a robot in unknown indoor settings via speech and visual interface—suitable for deploying in urban high‑rise common areas or rural homes
arXiv
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Humanoid teleoperation frameworks surveyed in literature provide system architectures combining sensor vision, remote control, and LLM‑enhanced decision‑making for tasks like waste detection and awareness messaging
arXiv
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Deployment scenarios
Remote/backward rural communities:
Robots travel via rough terrain or local transport to villages, perform litter pickup, show environmental education videos through built‑in screens or projection, and train local youth to maintain routines.
Neural networks for object detection (trash vs recyclable), LLMs for multilingual educational dialogue and FAQs.
Urban posh/skyscraper areas:
Deploy cleaning/awareness robots in common areas: lobbies, corridors, communal spaces.
Robots detect litter, perform bin collection, provide interactive reminders via speech or digital displays to building occupants about recycling, energy use, sustainability tips.
LLMs tailored on local languages provide real-time educational engagement (“Did you know…”) and answer questions.
Feasibility & challenges
Connectivity: needs decent network/edge compute in both remote and urban settings.
Training & maintenance: local tech support needed; educational programs like MBot could train local technicians.
Cost: humanoid robots are still expensive; modular low‑cost models reduce entry barrier. Collaboration with NGOs and governments to subsidize pilot deployments would help.
Summary
Historically, large‑scale environmental awareness and cleanup campaigns emerged mainly in the last ~60 years, across US, Canada, UK, Europe, Russia, Asia, Africa and Oceania—most visibly through movements like Earth Day, World Cleanup Day, Clean Games, national and local campaigns.
To bring such efforts into backward remote and upscale urban vertical areas, AI‑powered humanoid robots using neural‑network vision, LLM‑based educational interaction, and tele‑operation/autonomy (e.g. platforms like Teledrive, modular robotics like MBot) can be deployed—combining cleanup, recycling sorting, education, and community engagement.
If you’d like examples of specific robotic platforms or AI systems ready for this vision, or implementation steps for a particular region, I’d be glad to dive deeper!