Syllabus

🕘 Duration: 2 Days (16 Hours)

👥 Level: Intermediate to Advanced

💼 Target Participants:

  • Geospatial practitioners,
  • Agricultural/plantation researchers,
  • Data analysts,
  • Master’s/PhD students,
  • Technical staff in palm oil companies.

📋Method: Combination of lectures, hands-on practices, and case studies.

📚 Day 1 – Session 1: Introduction and Fundamentals of GeoAI

Topics:

  • Overview of GeoAI and Its Role in the Oil Palm Industry

  • Remote Sensing for Agriculture: UAV, Satellite, and Multispectral Data

  • Introduction to Monitoring Needs in Oil Palm Plantations

Learning Objectives:

  • Understand basic concepts of GeoAI and its applications in the palm oil sector.

  • Identify geospatial data sources (UAV, Sentinel-2, PlanetScope, etc.).

  • Explain business and technical requirements for monitoring oil palm plantations.

📚 Day 1 – Session 2: Data Acquisition and Preprocessing

Topics:

  • UAV Mission Planning and Data Collection

  • Orthomosaic Generation and Georeferencing

  • Satellite Imagery Preprocessing

  • Annotation and Labeling Techniques for AI Training

Learning Objectives:

  • Conduct UAV data acquisition for oil palm plantations.

  • Generate orthomosaic maps and perform geometric corrections.

  • Prepare imagery datasets for AI model training.

  • Use QGIS and LabelImg/Roboflow for data annotation and labeling.

📚 Day 2 – Session 1: AI for Oil Palm Tree Counting

Topics:

  • Deep Learning Fundamentals for Object Detection

  • CNN and YOLOv5/YOLOv8 for Tree Counting

  • Transfer Learning and Model Evaluation

  • Practice: Train a Model Using Oil Palm Dataset

Learning Objectives:

  • Understand object detection algorithms (YOLO, Faster R-CNN) for palm tree identification.

  • Train and evaluate AI models using UAV or satellite datasets.

  • Perform automated tree counting and validate results with field data.

📚 Day 2 – Session 2: Tree Health Assessment and Classification

Topics:

  • Vegetation Indices (NDVI, GNDVI, SAVI) and Tree Health Indicators

  • Semantic Segmentation with U-Net/DeepLab

  • Integrating Spectral and Textural Features

  • Case Study: Health Mapping in Oil Palm Plantations

Learning Objectives:

  • Utilize vegetation indices to assess oil palm tree health.

  • Train image segmentation models for classifying healthy/unhealthy areas.

  • Analyze classification results and validate with field or high-resolution data.

📚Day 2 – Session 3: GeoAI Dashboard & Deployment

Topics:

  • Spatial Analysis with Python (geopandas, rasterio, shapely)

  • Building a Simple GeoAI Dashboard (Streamlit or Dash)

  • Model Deployment and GIS Platform Integration

  • Group Project Presentation & Evaluation

Learning Objectives:

  • Integrate AI output with spatial data and map visualization.

  • Develop a dashboard prototype for monitoring oil palm plantations.

  • Design an end-to-end workflow from data acquisition to actionable insights.

  • Present project results and receive feedback from instructors.

Evaluation and Certification

  • Daily quizzes

  • Mini-project (individual or group)

  • Certificate awarded to participants with ≥80% attendance and who pass the final evaluation

Learning Outcomes:

By the end of the training, participants will be able to:

  • Understand the foundational concepts and benefits of GeoAI in the oil palm industry

  • Perform data acquisition, preprocessing, and analysis from UAVs, satellite imagery, and multispectral sensors

  • Apply deep learning models (YOLO, U-Net) to automatically count palm trees and assess their health

  • Integrate AI analysis with Geographic Information Systems (GIS)

  • Evaluate and validate model results using field data or high-resolution images

  • Apply an end-to-end pipeline from UAV mission planning to reporting insights for plantation management

Contact Information:
📧 wbudiharto@binus.edu
📱 Widodo (WA: +62 856-9887-384)
📱 Emny (WA: +62 813-8741-3863)

Silabus Pelatihan

Geospatial Artificial Intelligence (GeoAI) for Oil Palm Tree Counting and Health Assessment

🕘 Durasi: 2 Hari (16 jam)

👥Level: Intermediate to Advanced

💼  Metode: Teori (kuliah), praktik langsung (hands-on), dan studi kasus interaktif

📋Target Peserta:

  • Praktisi geospasial

  • Peneliti di bidang pertanian/perkebunan

  • Analis data

  • Mahasiswa S2/S3

  • Staf teknis perusahaan kelapa sawit

📚Hari 1 – Fondasi GeoAI dan Pengolahan Data Geospasial

Sesi 1: Pengenalan dan Dasar-dasar GeoAI

Topik:

  • Pengantar GeoAI dan perannya dalam industri kelapa sawit

  • Penginderaan jauh untuk pertanian: UAV, citra satelit, data multispektral

  • Kebutuhan pemantauan perkebunan kelapa sawit secara digital

Learning Outcomes:

  • Memahami konsep dan manfaat GeoAI

  • Mengenali berbagai sumber data geospasial (UAV, Sentinel-2, PlanetScope)

  • Menjelaskan kebutuhan teknis dan bisnis untuk pemantauan kelapa sawit

Sesi 2: Akuisisi dan Praproses Data

Topik:

  • Perencanaan misi UAV dan pengambilan data

  • Generasi orthomosaic dan georeferensi

  • Praproses citra satelit

  • Teknik anotasi dan labeling untuk pelatihan AI

Learning Outcomes:

  • Melakukan akuisisi data UAV secara efisien

  • Menghasilkan orthomosaic dan koreksi geometrik

  • Menyiapkan dataset untuk pelatihan model AI

  • Melakukan labeling menggunakan QGIS, LabelImg, atau Roboflow

📚 Hari 2 – Pemodelan AI dan Implementasi Lapangan

Sesi 1: Pendeteksian Pohon Kelapa Sawit dengan AI

Topik:

  • Dasar Deep Learning untuk Object Detection

  • Penerapan CNN dan YOLOv5/YOLOv8

  • Transfer learning dan evaluasi model

  • Latihan: Melatih model deteksi pohon

Learning Outcomes:

  • Memahami model deteksi objek (YOLO, Faster R-CNN)

  • Melatih model berbasis UAV/satelit untuk menghitung pohon

  • Melakukan perbandingan hasil deteksi dengan data lapangan

Sesi 2: Penilaian Kesehatan Pohon dan Klasifikasi

Topik:

  • Indeks vegetasi (NDVI, GNDVI, SAVI)

  • Segmentasi semantik dengan U-Net / DeepLab

  • Integrasi fitur spektral dan tekstural

  • Studi kasus: Health Mapping di perkebunan

Learning Outcomes:

  • Menggunakan indeks vegetasi untuk klasifikasi kesehatan pohon

  • Melatih dan menguji model segmentasi citra

  • Menganalisis hasil klasifikasi dan validasi lapangan

Sesi 3: Dashboard GeoAI dan Implementasi Sistem

Topik:

  • Analisis spasial dengan Python (GeoPandas, Rasterio, Shapely)

  • Membangun dashboard dengan Streamlit atau Dash

  • Integrasi model AI ke platform GIS

  • Presentasi proyek kelompok

Learning Outcomes:

  • Menggabungkan hasil AI dengan visualisasi spasial

  • Membangun prototipe dashboard monitoring

  • Menyusun workflow end-to-end dari data ke insight

  • Mendapatkan umpan balik atas hasil proyek

Evaluasi dan Sertifikasi

  • Kuis harian

  • Mini project (individu/kelompok)

  • Sertifikat diberikan kepada peserta yang:

    • Mengikuti ≥80% sesi

    • Lulus evaluasi akhir

Hasil Akhir Pelatihan

Setelah mengikuti pelatihan ini, peserta diharapkan mampu:

  • Memahami peran dan manfaat GeoAI dalam pengelolaan kelapa sawit

  • Melakukan akuisisi, praproses, dan analisis data geospasial dari UAV & citra satelit

  • Menerapkan model deep learning seperti YOLO dan U-Net untuk menghitung pohon dan menilai kesehatannya

  • Mengintegrasikan hasil AI ke dalam sistem GIS

  • Melakukan evaluasi dan validasi berbasis data lapangan

  • Menyusun pipeline GeoAI end-to-end: dari akuisisi hingga pelaporan

Kontak Informasi

📧 Email: wbudiharto@binus.edu
📱 Widodo (WA): 0856-9887-384
📱 Emny (WA): 0813-8741-3863