Top 5 location-based skiing apps you should have on your phone before you hit the slopes
It’s a middle of my skiing season and I’ve managed to test some of the coolest location-based skiing apps out there. From piste maps and avalanche conditions to sharing your location with your skiing buddies, these are the 5 top apps that you should install on your phone before hitting the slopes.
Fatmap
One of the best skiing apps ever. It features really precise 3D terrain maps for planning, recording and sharing your ski adventures. Fatmap offers advanced terrain analysis functions alike map of gradient as well as avalanche risk areas. Additionally for each winter resort available, it features phones to local rescue guys so that you wouldn’t have to search for it in case of emergency. Fatmap is definitely the ultimate toolkit for skiing both on and off-piste. You can download it for free but for the paid features you’ll have to pay $5-25 depending on the resort. The app is available for iOS and Android.
SkiLynx
Skiing is not only about hitting the slopes but also about hanging out with friends. SkiLynx is an app that keeps you connected with all of your skiing buddies wherever they are. The app lets you create a ski group allowing you to easily locate and communicate with your friends. SkiLynx tracks everyone is in a real-time, including details such as slope difficulty and percentage down the trail. There is a useful chat feature as well as the accompanying Apple Watch app, letting you quickly check in with everyone or set up a meeting in the middle of your skiing day. In addition SkiLynx features a regular GPS tracking and it records a variety of metrics which you can save and share over social networks. SkiLynx is available for iOS only and it costs $2.99.
Avanet
Skiing is not only about awesome lines and high speed but it’s also about avalanche safety. Avanet is the first global crowdsourced platform of mountain safety information. Designed to assist backcountry skiers and snowboarders as well as professional guides, Avanet uses GPS and map data to present users with geotagged observations by other mountain travelers that can warn you of hazards and potentially dangerous conditions, as well as contribute their own observations. Avanet can be used as a field safety aid and planning tool, navigation app, and your track logger. Avanet is a free iOS app.
Ski track
Ski tracks is one of the most popular skiing apps out there. It is very simple and you might find the graphics not too fancy but it gets the job done. The app collects a comprehensive field data, including maximum and average speed, total distance traveled and slope angle. All of these metrics can then be logged, divided into different runs and plotted out on a map. The app is available for iOS and Android and it costs $.99 but it’s worth it.
Endomondo
If you want to keep all your sport activities in a single app, Endomondo – one of the most popular fitness apps, gives you quite advanced skiing tracking features. The app takes advantage of GPS in a similar manner to tracking your jogging but it’s enough for the most of less advanced skiers. Endomondo offers a free apps for iOS, Android and Windows.
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29th February was a special day for us at Geoawesomeness, as we hosted our first Twitter Q&A #GeoChat with John Ristevski, Vice President of Reality Capture and Processing at HERE (Related: Calling all map lovers: What’s a #geochat and why you should participate). We had an awesome time during #GeoChat and in case you missed it, here’s a chronological summary of what happened.
The never ending waiting to kick-off the chat
Another 10 mins to go for #geochat and we are like https://t.co/uChVtQ3iGx pic.twitter.com/0UBCDg9HTy
— Geoawesomeness (@geoawesomeness) February 29, 2016
Good evening everyone! Welcome to #geochat. We are all set 🙂 https://t.co/WLdxgKpkzH
— Geoawesomeness (@geoawesomeness) February 29, 2016
Question 1:
Q1 #geochat @jaristevski When we say maps for machines, what exactly do we mean “machines” here & how different are they from maps we use?
— Geoawesomeness (@geoawesomeness) February 29, 2016
A1: could be an autonomous car or a drone or a robot #GeoChat
— John Ristevski (@jaristevski) February 29, 2016
A1: machine maps must be more accurate and less abstract. Machines can interpret more information #GeoChat
— John Ristevski (@jaristevski) February 29, 2016
The Twitter community is alive 😉
.@tillnm that's a great question! We can filter out moving objects after just one pass #GeoChat
— John Ristevski (@jaristevski) February 29, 2016
https://twitter.com/cartocalypse/status/704585875733225472
.@abuczkowski maps see beyond the sensing horizon of the machine #GeoChat
— John Ristevski (@jaristevski) February 29, 2016
Question 2:
Q2 #geochat @jaristevski How we do we create these HD maps? What level of accuracy and completeness is required by an #autonomouscar
— Geoawesomeness (@geoawesomeness) February 29, 2016
A2: we seed the creation of the HD Map with data from our True vehicle fleet equipped with LIDAR & cameras https://t.co/iyC05Pe1F4 #GeoChat
— John Ristevski (@jaristevski) February 29, 2016
@geoawesomeness radar and sonar are also useful for use cases like collision detection but not so much for mapmaking or guidance #GeoChat
— John Ristevski (@jaristevski) February 29, 2016
@geoawesomeness that's unclear. It will evolve as the technology and regulatory environment matures https://t.co/OuEGWdygvg #GeoChat
— John Ristevski (@jaristevski) February 29, 2016
Machines can use attributes like road slope and curvature. Humans just need to know how to get there #GeoChat
— John Ristevski (@jaristevski) February 29, 2016
20 mins have already go by! Twitter Q&As can be really fast 🙂
Question 3:
Q3 #geochat @jaristevski How we do prepare #autonomouscars to handle the unexpected? Can map makers be held accountable for accidents?
— Geoawesomeness (@geoawesomeness) February 29, 2016
A3: the more real-time the map is, the better prepared the machine will be for what it encounters #GeoChat
— John Ristevski (@jaristevski) February 29, 2016
A3: the key to keeping the map up-to-date is harnessing the sensor stream from connected vehicles with rich sensors #GeoChat
— John Ristevski (@jaristevski) February 29, 2016
Aleks decided to go rogue 😉 V2V Communications, Open Data and more…
The cars of the future will be able talk to each other #v2v. How will it influence future map making? #Geochat @jaristevski
— Aleks Buczkowski (@abuczkowski) February 29, 2016
V2V plays a big role for time-critical events but unlikely for mapmaking where V2-cloud will prevail due to aggregation #GeoChat
— John Ristevski (@jaristevski) February 29, 2016
How are AV w/ different datasets i.e #opensource and different mapping platforms going 2 talk 2 each other? @jaristevski #GeoChat @Eddysoft
— Nettie Ginocchetti ????? (@nettiequette) February 29, 2016
Check out our sensor ingestion protocol as an example of how we streamline communication https://t.co/qAuAXbP3HA #GeoChat
— John Ristevski (@jaristevski) February 29, 2016
Its time for Question 4:
Q4 #geochat @jaristevski How do we go about teaching #autonomouscars to drive along others & in diverse driving cultures eg. India & Germany
— Geoawesomeness (@geoawesomeness) February 29, 2016
A4: Autonomous cars need to understand human behavior to share the road. Big data from connected cars helps to reveal the patterns #GeoChat
— John Ristevski (@jaristevski) February 29, 2016
A4: I was just in India and there were no lane markings and the rules of the road are "flexible" #GeoChat
— John Ristevski (@jaristevski) February 29, 2016
A4: the map is even more important in places like India where sensor visibility is limited and the map can provide context #GeoChat
— John Ristevski (@jaristevski) February 29, 2016
A little fun on the side
@nettiequette Wonder if #autonomouscars will have “cultural” and language settings for comms with humans 😉 haha #GeoChat
— Geoawesomeness (@geoawesomeness) February 29, 2016
Back to the serious stuff…
.@jaristevski it means that it is a continuos learning process, maybe even sort of #AI. Can you also teach machines to make maps? #GeoChat
— Aleks Buczkowski (@abuczkowski) February 29, 2016
Can AI power the maps? Question 5:
.@abuczkowski has nailed Q5 #geochat @jaristevski Can we use #AI to map the world in HD? https://t.co/ej10Ek1teJ
— Geoawesomeness (@geoawesomeness) February 29, 2016
Yes! We use #machinelearning to mine the data collected from our mapping cars to extract HD map features #GeoChat
— John Ristevski (@jaristevski) February 29, 2016
.@jaristevski I guess that today it is still a semi-manual process. Will it ever be fully automated? #geochat
— Aleks Buczkowski (@abuczkowski) February 29, 2016
It will never be perfectly automated. People will always be part of the process #GeoChat
— John Ristevski (@jaristevski) February 29, 2016
Okay, its close to the end… Lets make things a bit more relaxed. Speaking of which,
Q6 #geochat @jaristevski If robots take over the world, can the Resistance upload false maps to save the world 😉 pic.twitter.com/pqr6psYTkF
— Geoawesomeness (@geoawesomeness) February 29, 2016
@jaristevski Okay, so the resistance has to time-travel then! Damn, thought we had a movie idea here 😉 #GeoChat
— Geoawesomeness (@geoawesomeness) February 29, 2016
Time to look into the crystal ball
When driverless cars will hit the roads all over the world in a massive scale? What is still needed/missing? #geochat @jaristevski
— Aleks Buczkowski (@abuczkowski) February 29, 2016
My 2 cents: 5 years for the first truly autonomous cars, 10 years in a meaningful scale #GeoChat
— John Ristevski (@jaristevski) February 29, 2016
And curtains!!
A big hurray for @jaristevski @here & everyone who participated in the 1st #GeoChat. Have a nice evening everyone 🙂 https://t.co/dcifzykRvo
— Geoawesomeness (@geoawesomeness) February 29, 2016
Stay tuned for the next #GeoChat 😉
