chore: fix typos across codebase

CHANGELOG.md

@@ -44,7 +44,7 @@
 
 - **order**: ordering on modelset
 - **id**: adds id filter in model
-- **gtlt**: greather than and leess than filter on timestamps
+- **gtlt**: greater than and less than filter on timestamps
 
 ## v1.1.3 (2024-10-04)
 

backend/README.md

@@ -58,7 +58,7 @@ export C_INCLUDE_PATH=/usr/include/gdal
 pip install --global-option=build_ext --global-option="-I/usr/include/gdal" GDAL==`gdal-config --version`
 ```
 
-- Install Ramp - Dependecies
+- Install Ramp - Dependencies
 ```
 cd ramp-code && cd colab && make install
 ```

backend/docs/backend_README.md

@@ -16,7 +16,7 @@ The RAMP model was trained with a particular focus on informal settlements and p
 
 The platform that fAIr provides is a further fine-tuning of the base-line model which enhance the model efficiacy to the desired area.
 
-> The backend codebase and the containersed docker enviornment is currently optimised for deployment on an Debian AWS instance with 4GB. Nvidia GPU and 16 GB. RAM.
+> The backend codebase and the containersed docker environment is currently optimised for deployment on an Debian AWS instance with 4GB. Nvidia GPU and 16 GB. RAM.
 
 > It is recommended to use the provided docker images and cloud instance to deploy the RAMP model. As the Docker images with model, model weights, and peripherial requirements will take up at least 10 Gb. of storage space.
 
@@ -72,7 +72,7 @@ You will need more env variables (Such as Ramp home, Training Home) that can be
 
 Now change your username, password and db name in settings.py accordingly to your database
 
-> The application is boostrapped by calling `manage.py` to bootstrap the geodjango application.
+> The application is bootstrapped by calling `manage.py` to bootstrap the geodjango application.
 
 ``` bash
 python manage.py makemigrations login

docs/Home.md

@@ -14,7 +14,7 @@
 - ### Data integration process.
   Another challenge faced by mappers is the integration process. Foexample, when using using Mapwithai, one has to always pick potions of data from the AI layer if they are buildings, one picks a maximum of less that 100 buildings and pastes them in the OSM data layer and does the same for all the buildings in that particular area or tile or layer.
 - ### Technical Expertise
-  The use of AI technology in mapping requires technical expertise, which can be a significant challenge for mappers who may not have the necessary skills. For instance, beginer Mappers can not be recommended or trained to use these AI tools as they may mess the entire area. Therefore frequent trainings are needed for mappers to be equipped with adquet skills of handling such tasks.
+  The use of AI technology in mapping requires technical expertise, which can be a significant challenge for mappers who may not have the necessary skills. For instance, beginner Mappers can not be recommended or trained to use these AI tools as they may mess the entire area. Therefore frequent trainings are needed for mappers to be equipped with adquet skills of handling such tasks.
 - ### Imagery offsets
   The AI technologies for example AI data from Microsoft was and is generated using Bing Imagery which in several cases is older compared to the recent Imageries like Maxar Premium. The imposes a big challenge in aligning the AI data with the latest imageries.
 <br>

docs/User-Manual-for-fAIr.md

@@ -102,7 +102,7 @@ https://github.com/hotosm/fAIr/assets/97789856/f4eae99f-e9a2-4424-806f-fdfce80e6
 3. Click on the button Create New.After clicking on 'Start Creating Dataset', click on the 'Create New' button to create a new dataset.
 <img width="1419" alt="3" src="https://github.com/hotosm/fAIr/assets/97789856/f1db017c-b360-42aa-bd1f-5c94049fc042">
 
-> A dataset would be a list of area of interests (AIOs) and its labels. OpenStreetMap data can be downlaoded automatically and used as initial
+> A dataset would be a list of area of interests (AIOs) and its labels. OpenStreetMap data can be downloaded automatically and used as initial
 > labels. It is our responsibility as model creators to make sure labels align with the feature before proceeding to training phase.
 
 4. Click on the input field , Give your Dataset Name.To name your dataset, click on the input field and type in your desired name & click on the 'Create Training Dataset'.
@@ -126,7 +126,7 @@ https://github.com/hotosm/fAIr/assets/97789856/f4eae99f-e9a2-4424-806f-fdfce80e6
 
 <img width="1413" alt="9" src="https://github.com/hotosm/fAIr/assets/97789856/ff61462e-02b3-4b23-8e8c-1c80c1582dd1">
 
-10. Fetch Exisiting OSM Buildings in your Area of Interest.Click on Fetch OSM Data button next to OSM Logo Inside List of Area of Interest on Right side.
+10. Fetch Existing OSM Buildings in your Area of Interest.Click on Fetch OSM Data button next to OSM Logo Inside List of Area of Interest on Right side.
 <img width="378" alt="10" src="https://github.com/hotosm/fAIr/assets/97789856/d318aca6-cdb1-43c4-b31a-1f19259201f7">
 
 11. Visualize each Buildings to check their accuracy.Zoom to Level 20 in Map to see OSM Buildings that you have just fetched.
@@ -173,7 +173,7 @@ https://github.com/hotosm/fAIr/assets/97789856/f4eae99f-e9a2-4424-806f-fdfce80e6
 <img width="890" alt="20" src="https://github.com/hotosm/fAIr/assets/97789856/5b860ce3-5c33-49f4-ad1f-33b47d4369b5">
 
 
-21.  Publish Your Training.Once you are statisfied accuracy and want to visualize its prediction you need to publish the training. You can run multiple trainings for same model to find best performing checkpoint, Each training will result different checkpoint. You can always publish another training. Click on PUblish Training button to Publish Model.
+21.  Publish Your Training.Once you are satisfied accuracy and want to visualize its prediction you need to publish the training. You can run multiple trainings for same model to find best performing checkpoint, Each training will result different checkpoint. You can always publish another training. Click on PUblish Training button to Publish Model.
 <img width="861" alt="21" src="https://github.com/hotosm/fAIr/assets/97789856/b9963566-c377-489d-8a3d-da0b7e835261">
 
 

frontend/docs/architecture/adr-choose-styling-library/adr3.md

@@ -38,7 +38,7 @@ We are building the fAIr website that requires a responsive and customizable use
 
 # Decision
 
-We've chosen to use Tailwind CSS, CSS3 and Shoelace to support the development of the platform because of the strict timeline and for future flexibility. Shoelace components (and sometimes HOT UI component (e.g Tracking/Cookie banner)) will be used extensively because it aligns with the future requirements of [HOT UI](https://github.com/hotosm/ui). Tailwind will be used occassionally in layout design and CSS3 via CSS modules will be used to support the designs when necessary.
+We've chosen to use Tailwind CSS, CSS3 and Shoelace to support the development of the platform because of the strict timeline and for future flexibility. Shoelace components (and sometimes HOT UI component (e.g Tracking/Cookie banner)) will be used extensively because it aligns with the future requirements of [HOT UI](https://github.com/hotosm/ui). Tailwind will be used occasionally in layout design and CSS3 via CSS modules will be used to support the designs when necessary.
 
 These three offer flexibility, speed of development, and also in tune with future requirements.
 

frontend/src/components/map/setups/setup-terra-draw.ts

@@ -32,7 +32,7 @@ export const setupTerraDraw = (map: maplibregl.Map) => {
     modes: [
       // new TerraDrawSelectMode({
       //   flags: {
-      //     arbitary: {
+      //     arbitrary: {
       //       feature: {},
       //     },
       //     rectangle: {