Three days later, Maya stood in front of the plantation management board with a presentation that changed everything.
Slide 1: Tree count accuracy — 96.3% validated against ground truth.
Slide 2: Age classification of palms across the concession, mapped precisely.
Slide 3: The spreading disease cluster, highlighted in red, with a recommended intervention zone drawn automatically.
Slide 4: Yield estimation models based on crown size and health metrics.
The operations director, a weathered man named Pak Hendra who had been skeptical of "satellite magic" for twenty years, stared at the screen for a long time. download ecognition oil palm application 2.0
"How much would it cost us to lose that section?" he asked quietly.
"Approximately 2.4 million rupiah per hectare per year," Maya said. "Times 34 hectares. And growing."
Hendra removed his glasses and rubbed his eyes.
"We need this on every concession."
Research or project repositories
Contact the developer/team
The next morning, Maya loaded a drone image set from a 2,000-hectare concession near Riau Province. The imagery was decent — 10 cm resolution — but the canopy was dense, and shadows from morning fog made half the trees look like dark smudges. Three days later, Maya stood in front of
Old Maya would have spent two weeks manually counting trees.
eCognition Oil Palm Application 2.0 processed the entire area in 47 minutes.
She leaned back in her chair, blinking.
The software had identified 14,312 individual palms — breaking them into crown categories: healthy, stressed, immature, and dead. Each tree was outlined. Each tree had coordinates. Each tree had a health score.
"What the hell," she whispered.
Extract the ZIP archive to a local folder, for example:
C:\eCognition\Applications\OilPalm_v2.0\
The extracted contents include:
| File/Folder | Description |
|-------------|-------------|
| OilPalm_v2.0.dcp | Main rule set file |
| /algorithms/ | Custom feature algorithms (Python script for age estimation) |
| /sample_data/ | Example image subset (500x500 px) for testing |
| user_guide_v2.0.pdf | Step-by-step workflow and parameter tuning guide |
| release_notes_v2.0.txt | Changes from version 1.x |
eCognition Oil Palm Application 2.0 is a domain-specific project built on the eCognition platform for automated oil-palm plantation mapping and analysis. The application typically includes rule sets, object-based image analysis workflows, and example datasets enabling classification of oil palm plantations from high- or very-high-resolution satellite or aerial imagery.
Before diving into the download process, it is critical to understand why this specific application is the industry gold standard. Unlike pixel-based classification (found in ERDAS or ArcGIS), eCognition uses Object-Based Image Analysis (OBIA). For oil palm, which features repetitive, textured canopies of varying ages, OBIA is the only method that accurately distinguishes between young palms, mature palms, senescent trees, and ground cover. Research or project repositories
What’s new in Version 2.0?
SSBBW
