AI is growing fast and transforming numerous industries. Machine learning has become more popular today due to ever-increasing data volumes, advanced algorithms, and improvements in computing power and storage. Machine learning has improved computer vision about recognition and tracking. In recent years, there has been an increase in research on object detection, image instance segmentation, video object tracking, video object detection, video semantic segmentation, and video object segmentation. …


AI has become more popular today due to ever-increasing data volumes, advanced algorithms, and improvements in computing power and storage. The integration of artificial intelligence (AI) with GIS (geographic information systems) creates a new scientific discipline so-called geospatial artificial intelligence (GeoAI). By integrating machine learning with GIS technologies, it is possible to perform insightful spatial big data analyses to deal with unprecedented traffic, pollution, energy demands, environmental issues, public health, and safety concerns in modern cities.

I was recently involved with a solar mapping project about assessing the solar energy potential of building roofs and providing information about solar panel…

Detecting Green Roofs in Toronto


As geospatial data becomes more ubiquitous, processing geospatial big data has become an essential part of big data analytics. The amount of data is increasing at an exponential rate. Geospatial big data (2D, 3D, point cloud) processing has always been a challenge not only in the information and technology (IT) sectors but also in the geospatial domain. Efficiently handling geospatial data is essential for extracting meaningful information from big data. Big data processing techniques analyze big datasets at terabyte or even petabyte scale. In many cases, we need to use a combination of different tools and approaches to process geospatial…

Can GIS Analysis Make Toronto Roads Safer?


Being the largest city in Canada, road safety is the main concern of everyone living in Toronto. Based on Toronto Police Service KSI data, there were 610 fatalities including 351 pedestrians, 109 drivers, 48 motorcycle drivers and 30 cyclists for the period 2008–2018. Since 2017, Toronto city has implemented an ambitious comprehensive five-year (2017–2021) Vision Zero Road Safety Plan and adopted various road safety measures and campaigns aiming to eliminate traffic-related fatalities and serious injuries. The Plan identifies and addresses six emphasis areas, which were determined through collision data analysis, public engagement and Council direction. …

Record Extract Limit

Have you ever encountered a limitation of 1000 or 2000 features when you extracted data from REST service endpoint, either through ArcGIS Online, Server or Portal?

import urllib.request, urllib.error, urllib.parse
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
import io, sys
import json, requests
from json import loads
from shapely.geometry import Point, Polygon
import numpy as np
import ssl
from shapely.geometry import shape
from import json_normalize
from collections import OrderedDict
# remove warnings
import warnings
# set url
baseURL = ""
fields = "*"
# Get record extract limit
urlstring = baseURL + "?f=json"
j = urllib.request.urlopen(urlstring)
js = json.load(j)
maxrcn = int(js["maxRecordCount"])
print(("Record extract limit: %s" % maxrcn))…

UrbanAccess Toronto Demo

Toronto UrbanAccess Network


UrbanAccess is a tool for creating multi-modal graph networks for use in multi-scale (e.g. address level to the metropolitan level) transit accessibility analyses with the network analysis tool Pandana. UrbanAccess provides a generalized, computationally efficient, and unified accessibility calculation framework by linking tools for 1) network data acquisition, validation, and processing; 2) computing an integrated pedestrian and transit weighted network graph, and 3) network analysis using Pandana.

It can be used for computing GTFS transit and OSM pedestrian networks for accessibility analysis. Here, I would like to introduce the main functionality of UrbanAccess with examples using Toronto’s GTFS data and…

Detecting Swimming Pools

In my previous post, I covered how to detect solar panels on building rooftops using Deep Learning in ArcPro. Today I would like to share one of my previous work samples about detecting swimming pools using Deep Learning in ArcPro. Creating Deep Learning Framework in ArcPro has already been stated in my previous post, therefore, they are not repeated here. Let’s get started by preparing training sample data.

Data Preparation: Create classes and label objects

All supervised Deep Learning tasks depend on labelled datasets. Image annotation, or labelling, is vital for deep learning tasks such as computer vision and learning. The Label Objects for Deep Learning tool…


Walkability has been a major focus of urban analytics. Walkable neighborhoods are directly correlated with better health, a lighter environmental impact, cost-efficient living, and an overall better quality of life. It can be a measure to see how friendly an area is for walking by looking at what is around an area and how easy it is to access amenities such as school, hospital, library, stops, stations, arena, supermarket, stores within a specific walking time. …



The coronavirus, called Covid-19 by the WHO, is continuing to spread around the world. As of April 28, 2020, COVID-19 has infected more than 3.13 million people and killed at least 217,000 people worldwide. In this tutorial, we will learn how to map the virus spread using python libraries (Folium, Geopandas, Pandas, Numpy, hvplot, Matplotlib, that can be helpful for data wrangling, and mapping COVID-19 data.

Data Source

There are a lot of COVID-19 datasets available such as Worldometer, Our World in Data (cumulative), and some other COVID-19 datasets from Google, Johns Hopkins University, European Union Open Data Portal. Worldometer provides…

Ablajan Sulaiman

Senior Geospatial Specialist in Toronto

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