Sr. GIS and Remote Sensing

Semantic Technologies & Agritech Services Private Limited; Maharashtra; On-site; INR 800,000 - 1,200,000; Full time

Job summary

Semantic Technologies & Agritech Services Private Limited is seeking a skilled GIS & Remote Sensing Specialist to leverage satellite imagery for agricultural analytics, crop monitoring, and phenology tracking.

The role focuses on processing multispectral data (Sentinel-2, Landsat, Planet), applying vegetation indices (NDVI, EVI, LAI), building ML/DL models for crop classification and yield prediction, and delivering geospatial insights to support agritech solutions.

Qualifications

• Experience in agricultural applications and satellite image processing.
• Proficient in multispectral and hyperspectral imagery processing.
• Experience with vegetation indices NDVI, EVI, LAI for crop health monitoring.
• Experience with ML models for land-cover mapping and crop classification.
• Experience with CNN or U-Net segmentation for agricultural images.
• Geospatial analysis and raster/vector processing workflows.

Responsibilities

• Process multispectral and hyperspectral satellite imagery for agricultural applications.
• Develop ML/DL models for crop classification and yield prediction.
• Perform time-series analysis for crop growth and phenology tracking.
• Collaborate with software and agricultural teams to deliver geospatial outputs.

Skills

GIS & Remote Sensing

Agricultural Remote Sensing

Machine Learning

Deep Learning

Spatial Analytics

Time-series analysis

Tools

ArcGIS Pro/ArcMap

QGIS

ERDAS Imagine

TensorFlow

PyTorch

Scikit-Learn

Job description

We are looking for a skilled GIS & Remote Sensing Specialist with experience in agricultural applications, satellite image processing, geospatial analysis, and machine learning. The role involves working with satellite imagery and spatial datasets to support crop monitoring, crop classification, crop health assessment, and agricultural analytics.

Role Overview

We are looking for a skilled GIS & Remote Sensing Specialist with experience in agricultural applications, satellite image processing, geospatial analysis, and machine learning. The role involves working with satellite imagery and spatial datasets to support crop monitoring, crop classification, crop health assessment, and agricultural analytics.

Key Responsibilities

• Process and analyze multispectral and hyperspectral satellite imagery from sources such as Sentinel-2, Landsat, and Planet for agricultural applications.
• Generate and analyze vegetation indices such as NDVI, EVI, and LAI for crop health, vigor, and stress monitoring.
• Perform crop classification, crop mapping, phenology tracking, and yield prediction using remote sensing and GIS techniques.
• Develop and implement machine learning models such as Random Forest and SVM for land-cover mapping and multi-class crop classification.
• Apply deep learning techniques, including CNN and U-Net-based semantic segmentation, for agricultural image analysis and extraction of crop boundaries.
• Perform geospatial analysis and spatial data processing using raster and vector datasets.
• Execute advanced GIS operations, spatial queries, geoprocessing, and workflow development using ArcGIS Pro/ArcMap, QGIS, and ERDAS Imagine.
• Conduct image segmentation and time-series analysis to identify crop growth patterns and changes throughout the crop season.
• Prepare, validate, and analyze spatial datasets for agricultural and remote sensing projects.
• Collaborate with software, GIS, and agricultural teams to develop geospatial solutions and analytical outputs for project requirements.

Technical Skills

• GIS & Remote Sensing: ArcGIS Pro, ArcMap, QGIS, ERDAS Imagine
• Agricultural Remote Sensing: NDVI, EVI, LAI, crop health monitoring, crop classification, phenology monitoring, yield prediction
• Machine Learning: Random Forest, SVM, Scikit-Learn
• Deep Learning: CNN, U-Net, TensorFlow, PyTorch
• Spatial Analytics: Raster and vector processing, geospatial analysis, image segmentation, spatial querying, time-series analysis