Soumyadip_Chandra

Soumyadip Chandra
Objective
To secure a challenging position where I can contribute my skills.
EXPERIENCE
December-24, 2012 — Current
1) Organization’s Name: Divensi Solutions Pvt. LtD
Designation: Team lead (LiDAR)
Projects:
ROAD CORRIDOR, DOT PROJECTS (MOBILE MAPPING)
a) Classifying the ground points in the LiDAR data.
b) Verifying the ground data by generating a contour.
c) Extracting break lines for different features associated with the road like:
i) Center line of road
ii) Edge of bitumen
iii) Either edges of paint line.
iv) Edge of shoulder of road.
v) Bottom of road divider
vi) Kerb line of road
vii) Guard rails of road.
viii) Flyovers and underpasses.
ix) Road furniture like lamp post, mile stones, sign boards, billboards.
x) Road shot and ground shot generation
d) TIN Generation
AS-BUILT MODELING, LIDAR (STATIC SCAN)
The purpose was modeling of a dam for 3D printing.
a) Modeled all the features in DAM including dam and spill gates.
b) All the models were perfectly matched with point cloud.
c) Topology (Snap, union, etc.) between the different solids were checked.
d) It was ensured the complete model is water tight otherwise it will not be acceptable by 3D
printer.
3D MODELING, LIDAR (MOBILE MAPPING)
3D modeling of different features both side of the trajectory of Mobile Mapping scanner. The
scanning was done along the railway corridor. The aim of this project is preparing the simulation of
railway corridor along with all the features.
a) Modeling of 3D objects (Over bridge, Footbridge, Tunnel, Building, Platform) using MSTN
(V8i) and Terrasolid package.
b) Modeling of linear features like fence, walls according to the point cloud. Also Video was
used to compare the thickness of the linear features.
c) Extraction of rail tracks(3D) from point clouds.
d) Extraction of breaklines.
e) Generation of mesh. It is generated in such a way that all the features perfectly fit.
Mobile: +91-9337104803, E-Mail – soumyaiitk10@gmail.com, soumya_cu@yahoo.com,

Soumyadip Chandra
September - 19, 2011— December-24, 2012
1) Organization’s Name: COWI India Pvt. Ltd
Designation: Project Coordinator
Projects:
POWER LINE CORRIDOR PROJECTS
• US Power line
This project was specifically classification of power line corridor in conformance to US standards.
Data Preparation:
a. PTC file creation as per client’s specification
b. Running pre classification macro
c. Object dgn creation from master dgn containing all the levels.
d. Digitization of centerline. Crossing and substation
e. Creating buffer
Feature Coding:
a. Classification of features on ground and overhead.
b. Ground features are further segregated to manmade and natural features.
c. Power line infra is the features referred to as Over Head Details.
Stringing:
a. Detecting the power lines amongst the point cloud and drawing the catenaries representing
the transmission lines or conductors.
b. Placing the top and bottom at the center of each structure mass.
c. Deriving the POAs for points where a conductor meets the structure or as specified by the
client.
• Other Power line projects done
i) North American Power line projects
ii) European Power line projects
GROUND EDITING LIDAR (AIRBORNE)
Ground editing or deriving the LiDAR returns defining the terrain, Lithuania.
a. Classification of LiDAR points into Ground and non-ground class by means of an
automatic routine.
b. Surface generation and visual inspection followed up by manual editing to fix the errors left
by automatic classification.
c. Classifying the low points, high points, isolated or noise points in the data.
d. Generating the contours (minor at 0.5m and basic at 1 Meter interval) and editing it where
ever necessary.
e. Drawing break lines in the areas where there is void in the ground LiDAR data like at
streams, lakes, forest areas.
f. Exporting the data to different required format like Arc Grid, ASCII XYZ etc.
g. Generating the hill shades and ensuring consistency across the plan edges.
h. Exporting the intensity images of LiDAR blocks.

Soumyadip Chandra
i. Hydro flattened DTM.
Similar other ground editing projects:
 Denmark
 Austria
 Appenzel.
 AHN, Netherland
 LPA
 Norway
 European Union
 NZAM
ORTHOMOSAIC GENERATION AND DATA CALIBRATION (Airborne)
Ortho photo generation and calibration of LiDAR raw data, UK
a) Flight line matching
b) H, R, P, M correction
c) Camera calibration
d) Quick ortho generation
e) True ortho generation
f) Color balancing
g) Seam line matching
Responsibilities:
 Handling a team for different project.
 Implementing the standard plan to keep up the productivity.
 Preparation of estimate of any new project.
 Preparation of quality and project plan.
 Communication with foreign clients.
 Responsibility for effective flow of information between the team members.
 Project planning, scheduling of the delivery and resource management.
 Evaluation on project activity report on project progress.
 Defining and developing the process-flow for a job.
 Establish the quality requirement for the project and sharing it with the client before
commencement of a project in form of a quality plan document.
 Based upon the quality plan developing an AQL (Acceptable quality levels) for the project
and sharing it with the production shop floor.
 Delivering exact quantity with quality in time as per the schedule.
 Identifying the wastes (in a process) in a project and get rid of them as soon as possible in
order to maximize the efficiency.
September, 2010 — September- 18, 2011
2) Organization’s Name: IIT Kanpur
Designation: Sr. Project Associate
Responsibilities:
a) Algorithm developing for intensity normalization of LiDAR data

Soumyadip Chandra
In this project the intensity calibration of airborne laser scanner data is performed without
using any known brightness targets. The data was calibrated with itself using a calibration
formula developed by Finnish Geodetic Institute. The key concept of this calibration
process is objects with same material should always show same intensity values. But due to
distance from the scanner, scan angle and object geometry this does not happen often. The
average flying height was about 1100 meter above ground level. Intensity values need to be
corrected with respect to range, incidence angle, atmospheric transmittance and transmitted
power (because difference in PRF will lead to different transmitter power values). After these
corrections, the intensity values were directly relative to target reflectance. The algorithm has
shown promising result.
b) 3D modeling using LiDAR data
c) Integration of GPS and IMU
d) Terrestrial laser scanner (TLS) data processing
SKILL SETS
• Languages: Matlab, C, C++
• Softwares:
Image processing: ERDAS Imagine (8.5 & 9).
LiDAR: Bentley Microstation (V8 & V8i), Terrascan, Terramodeler, Terraphoto,
Terramatch, Global Mapper, E3DE (ENVI), LasTools, Lasviewer, LASUtility,
Google earth.
GIS: ArcGIS, Quantum GIS
• Equipment Expertise: Total Station, Terrestrial Laser Scanner, GPS, Geodetic GPS,
Automatic and Digital Levels,
EDUCATION
COURSE INSTITUTE YEAR %/CPI
M.Tech
(Civil Engg. - Geomatics)
IIT Kanpur 2010 7.5
M.Sc
(Applied Mathematics)
University of Calcutta 2007 55.2%
B.Sc. – Mathematics (Honors) Narendrapur Ramakrishna Mission,
University of Calcutta
2005 63.9%
INTERMEDIATE Bhagawanpur High School (WB Board) 2001 71.3%
High School Bhagawanpur High School (WB Board) 1999 76.5%

Soumyadip Chandra
M.TECH TERM PAPERS
SUBJECT NAME
Geospatial Data Bancroft Process-The Noniterative Solution
for GPS Receiver position
Remote Sensing Remote sensing for prediction of earthquake
Geological Hazards Konkan Landslides
Precision Remote Sensing Forest inventory using Airborne LiDAR.
Machine Processing of Remotely Sensed
Data
Developed Matlab code for Independent
Component Analysis for Image classification
ACADEMIC PROJECTS
M.TECH THESIS (JULY’09-JULY’10)
UNSUPERVISED AND SUPERVISED LEARNING WITH HYPER-SPECTRAL DATA
ORGANIZATION: INDIAN INSTITUTE OF TECHNOLOGY KANPUR
Project Detail: Hyper-Spectral data (HD) classification is a challenging job with respect to
multispectral data set due to high dimensionality of data set. The data set are highly
correlated. So some feature extraction process should be applied to reduce the
dimensionality of the data set. The objectives of this thesis are to investigate the extension
to which advanced classifier can reduce the problems of classification for hyperspectral data
and Investigation of best feature reduction techniques for hyperspectral data classification,
conventional as well as advanced classifier.
After performing different experiments it was confirmed that HDresponses best with
segmented principal component analysis (SPCA)(Feature extraction technique). After
applying SPCA, Support vector machine with quadratic programming optimizer provides
best classification result than any other feature extractors and classifiers with statistically
large set of training pixels. But for statistically sufficient set of training pixels SVM’s
performance is not statistically better than parametric classifier like Gaussian Maximum
likelihood (GML). It is strongly recommended that for statistically sufficient set of training
pixels GML should be applied for classification of HD. For both the case SPCA is the best
feature extraction techniques among all others.
Another problem of classification is time requirement. With large set of training sample
HD requires large training time. But this process reduces classification time significantly.
Still now this proposal is the best for HD classification
Programming Tools: Matlab
ACHIEVEMENTS
• Secured 2nd
highest CPI in Geoinformatics in M.Tech. Batch 2008.

Soumyadip Chandra
• Secured 93.4 percentile and 152 rank in GATE-2008
EXTRA-CURRICULAR ACTIVITIES
• Member of core team of Society of Civil Engineers (SOCE).
• Assistant Secretary of the 47th
Reunion of “Mixed and Applied Mathematics
Students’ Association” at 2007, Calcutta University, Department of Applied
Mathematics.
INTERESTS
• Playing Guitar, Skating.
PERSONAL PROFILE
Father’s Name :Mr. Dilip Kumar Chandra
Gender :Male
Nationality :Indian
Date of Birth :23/01/1984
Permanent Address :VILL-Benudia,P.O+P.S-Bhagwanpur,
DIST-Midnapur(E),STATE-WestBengal,
PIN-721601
REFERENCES
Dr. Onkar Dikshit
Professor
Department. Of Civil Engineering
Indian Institute of Technology Kanpur.
onkar@iitk.ac.in
Dr. Bharat Lohani
Professor
Civil Engineering Department.
Indian Institute of Technology Kanpur.
blohani@iitk.ac.in

Soumyadip_Chandra

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